Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations79007
Missing cells513914
Missing cells (%)17.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory156.3 MiB
Average record size in memory2.0 KiB

Variable types

Text6
Categorical25
DateTime1
Boolean1
Numeric4

Alerts

Off-Road Description has constant value "Parking Lot of 10401 Fernwood Rd" Constant
Driver Substance Abuse is highly overall correlated with Hit/RunHigh correlation
Hit/Run is highly overall correlated with Driver Substance AbuseHigh correlation
Latitude is highly overall correlated with LongitudeHigh correlation
Longitude is highly overall correlated with LatitudeHigh correlation
Municipality is highly overall correlated with Number of LanesHigh correlation
Non-Motorist Substance Abuse is highly overall correlated with Number of LanesHigh correlation
Number of Lanes is highly overall correlated with Municipality and 2 other fieldsHigh correlation
Related Non-Motorist is highly overall correlated with Number of LanesHigh correlation
Agency Name is highly imbalanced (65.8%) Imbalance
Distance Unit is highly imbalanced (74.3%) Imbalance
Road Grade is highly imbalanced (61.3%) Imbalance
Municipality is highly imbalanced (54.2%) Imbalance
Related Non-Motorist is highly imbalanced (63.2%) Imbalance
At Fault is highly imbalanced (79.0%) Imbalance
Weather is highly imbalanced (63.9%) Imbalance
Surface Condition is highly imbalanced (73.6%) Imbalance
Light is highly imbalanced (51.6%) Imbalance
Traffic Control is highly imbalanced (52.0%) Imbalance
Driver Substance Abuse is highly imbalanced (72.8%) Imbalance
Non-Motorist Substance Abuse is highly imbalanced (80.8%) Imbalance
First Harmful Event is highly imbalanced (68.1%) Imbalance
Second Harmful Event is highly imbalanced (57.1%) Imbalance
Intersection Type is highly imbalanced (54.6%) Imbalance
Road Alignment is highly imbalanced (69.3%) Imbalance
Road Condition is highly imbalanced (95.2%) Imbalance
Lane Type has 70174 (88.8%) missing values Missing
Off-Road Description has 79006 (> 99.9%) missing values Missing
Municipality has 69268 (87.7%) missing values Missing
Related Non-Motorist has 75134 (95.1%) missing values Missing
Weather has 6146 (7.8%) missing values Missing
Surface Condition has 2175 (2.8%) missing values Missing
Traffic Control has 10846 (13.7%) missing values Missing
Driver Substance Abuse has 12130 (15.4%) missing values Missing
Non-Motorist Substance Abuse has 75950 (96.1%) missing values Missing
Second Harmful Event has 58373 (73.9%) missing values Missing
Junction has 12783 (16.2%) missing values Missing
Intersection Type has 34145 (43.2%) missing values Missing
Road Condition has 3138 (4.0%) missing values Missing
Road Division has 1141 (1.4%) missing values Missing
Report Number has unique values Unique
Distance has 38659 (48.9%) zeros Zeros

Reproduction

Analysis started2025-02-12 01:35:09.511413
Analysis finished2025-02-12 01:36:11.382786
Duration1 minute and 1.87 second
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Report Number
Text

Unique 

Distinct79007
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
2025-02-12T01:36:11.700504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.869606
Min length10

Characters and Unicode

Total characters858775
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79007 ?
Unique (%)100.0%

Sample

1st rowMCP1123002M
2nd rowMCP21610009
3rd rowMCP2790000P
4th rowMCP3378000J
5th rowDD5659000H
ValueCountFrequency (%)
mcp1123002m 1
 
< 0.1%
mcp22510019 1
 
< 0.1%
mcp3378000j 1
 
< 0.1%
dd5659000h 1
 
< 0.1%
mcp33190021 1
 
< 0.1%
mcp3008003z 1
 
< 0.1%
mcp289200fc 1
 
< 0.1%
mcp2821002x 1
 
< 0.1%
mcp2771002w 1
 
< 0.1%
mcp08990036 1
 
< 0.1%
Other values (78997) 78997
> 99.9%
2025-02-12T01:36:12.249798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 202983
23.6%
2 77487
 
9.0%
M 72793
 
8.5%
C 71964
 
8.4%
P 71358
 
8.3%
3 56139
 
6.5%
1 55058
 
6.4%
5 33675
 
3.9%
8 30983
 
3.6%
6 30507
 
3.6%
Other values (23) 155828
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 574571
66.9%
Uppercase Letter 284204
33.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 72793
25.6%
C 71964
25.3%
P 71358
25.1%
D 14199
 
5.0%
J 6246
 
2.2%
E 3485
 
1.2%
B 3462
 
1.2%
H 3393
 
1.2%
F 2983
 
1.0%
G 2907
 
1.0%
Other values (13) 31414
11.1%
Decimal Number
ValueCountFrequency (%)
0 202983
35.3%
2 77487
 
13.5%
3 56139
 
9.8%
1 55058
 
9.6%
5 33675
 
5.9%
8 30983
 
5.4%
6 30507
 
5.3%
9 29975
 
5.2%
7 29333
 
5.1%
4 28431
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 574571
66.9%
Latin 284204
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 72793
25.6%
C 71964
25.3%
P 71358
25.1%
D 14199
 
5.0%
J 6246
 
2.2%
E 3485
 
1.2%
B 3462
 
1.2%
H 3393
 
1.2%
F 2983
 
1.0%
G 2907
 
1.0%
Other values (13) 31414
11.1%
Common
ValueCountFrequency (%)
0 202983
35.3%
2 77487
 
13.5%
3 56139
 
9.8%
1 55058
 
9.6%
5 33675
 
5.9%
8 30983
 
5.4%
6 30507
 
5.3%
9 29975
 
5.2%
7 29333
 
5.1%
4 28431
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 202983
23.6%
2 77487
 
9.0%
M 72793
 
8.5%
C 71964
 
8.4%
P 71358
 
8.3%
3 56139
 
6.5%
1 55058
 
6.4%
5 33675
 
3.9%
8 30983
 
3.6%
6 30507
 
3.6%
Other values (23) 155828
18.1%
Distinct78941
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
2025-02-12T01:36:12.542556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.7177845
Min length4

Characters and Unicode

Total characters688766
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78875 ?
Unique (%)99.8%

Sample

1st row190010046
2nd row16028039
3rd row15041420
4th row230051006
5th row230049130
ValueCountFrequency (%)
230054902 2
 
< 0.1%
220002746 2
 
< 0.1%
220044049 2
 
< 0.1%
15000418 2
 
< 0.1%
17001214 2
 
< 0.1%
220045986 2
 
< 0.1%
190013430 2
 
< 0.1%
220031288 2
 
< 0.1%
16055120 2
 
< 0.1%
16000781 2
 
< 0.1%
Other values (78931) 78987
> 99.9%
2025-02-12T01:36:13.036575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 181801
26.4%
1 100239
14.6%
2 83870
12.2%
5 58390
 
8.5%
3 52353
 
7.6%
6 46417
 
6.7%
4 43838
 
6.4%
7 41537
 
6.0%
8 40193
 
5.8%
9 40119
 
5.8%
Other values (2) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 688757
> 99.9%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181801
26.4%
1 100239
14.6%
2 83870
12.2%
5 58390
 
8.5%
3 52353
 
7.6%
6 46417
 
6.7%
4 43838
 
6.4%
7 41537
 
6.0%
8 40193
 
5.8%
9 40119
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
P 8
88.9%
M 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 688757
> 99.9%
Latin 9
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181801
26.4%
1 100239
14.6%
2 83870
12.2%
5 58390
 
8.5%
3 52353
 
7.6%
6 46417
 
6.7%
4 43838
 
6.4%
7 41537
 
6.0%
8 40193
 
5.8%
9 40119
 
5.8%
Latin
ValueCountFrequency (%)
P 8
88.9%
M 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 688766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181801
26.4%
1 100239
14.6%
2 83870
12.2%
5 58390
 
8.5%
3 52353
 
7.6%
6 46417
 
6.7%
4 43838
 
6.4%
7 41537
 
6.0%
8 40193
 
5.8%
9 40119
 
5.8%
Other values (2) 9
 
< 0.1%

Agency Name
Categorical

Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Montgomery County Police
63828 
MONTGOMERY
 
4876
Rockville Police Departme
 
4550
Gaithersburg Police Depar
 
3315
Takoma Park Police Depart
 
1294
Other values (5)
 
1144

Length

Max length25
Median length24
Mean length23.152404
Min length6

Characters and Unicode

Total characters1829202
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMontgomery County Police
2nd rowMontgomery County Police
3rd rowMONTGOMERY
4th rowMontgomery County Police
5th rowRockville Police Departme

Common Values

ValueCountFrequency (%)
Montgomery County Police 63828
80.8%
MONTGOMERY 4876
 
6.2%
Rockville Police Departme 4550
 
5.8%
Gaithersburg Police Depar 3315
 
4.2%
Takoma Park Police Depart 1294
 
1.6%
Maryland-National Capital 569
 
0.7%
ROCKVILLE 300
 
0.4%
GAITHERSBURG 170
 
0.2%
TAKOMA 70
 
0.1%
MCPARK 35
 
< 0.1%

Length

2025-02-12T01:36:13.185685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:13.346765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
police 72987
32.2%
montgomery 68704
30.3%
county 63828
28.1%
rockville 4850
 
2.1%
departme 4550
 
2.0%
gaithersburg 3485
 
1.5%
depar 3315
 
1.5%
takoma 1364
 
0.6%
park 1294
 
0.6%
depart 1294
 
0.6%
Other values (3) 1173
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 270884
14.8%
e 158389
 
8.7%
147837
 
8.1%
t 137953
 
7.5%
n 128794
 
7.0%
y 128225
 
7.0%
l 83794
 
4.6%
i 81990
 
4.5%
r 81480
 
4.5%
c 77537
 
4.2%
Other values (32) 532319
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1404704
76.8%
Uppercase Letter 276092
 
15.1%
Space Separator 147837
 
8.1%
Dash Punctuation 569
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 270884
19.3%
e 158389
11.3%
t 137953
9.8%
n 128794
9.2%
y 128225
9.1%
l 83794
 
6.0%
i 81990
 
5.8%
r 81480
 
5.8%
c 77537
 
5.5%
m 69672
 
5.0%
Other values (10) 185986
13.2%
Uppercase Letter
ValueCountFrequency (%)
P 74316
26.9%
M 74254
26.9%
C 64732
23.4%
O 10122
 
3.7%
R 10101
 
3.7%
D 9159
 
3.3%
G 8531
 
3.1%
T 6410
 
2.3%
N 5445
 
2.0%
E 5346
 
1.9%
Other values (10) 7676
 
2.8%
Space Separator
ValueCountFrequency (%)
147837
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 569
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1680796
91.9%
Common 148406
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 270884
16.1%
e 158389
 
9.4%
t 137953
 
8.2%
n 128794
 
7.7%
y 128225
 
7.6%
l 83794
 
5.0%
i 81990
 
4.9%
r 81480
 
4.8%
c 77537
 
4.6%
P 74316
 
4.4%
Other values (30) 457434
27.2%
Common
ValueCountFrequency (%)
147837
99.6%
- 569
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1829202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 270884
14.8%
e 158389
 
8.7%
147837
 
8.1%
t 137953
 
7.5%
n 128794
 
7.0%
y 128225
 
7.0%
l 83794
 
4.6%
i 81990
 
4.5%
r 81480
 
4.5%
c 77537
 
4.2%
Other values (32) 532319
29.1%

ACRS Report Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
Property Damage Crash
49682 
Injury Crash
29056 
Fatal Crash
 
269

Length

Max length21
Median length21
Mean length17.656068
Min length11

Characters and Unicode

Total characters1394953
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInjury Crash
2nd rowProperty Damage Crash
3rd rowProperty Damage Crash
4th rowInjury Crash
5th rowProperty Damage Crash

Common Values

ValueCountFrequency (%)
Property Damage Crash 49682
62.9%
Injury Crash 29056
36.8%
Fatal Crash 269
 
0.3%

Length

2025-02-12T01:36:13.529805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:13.621043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
crash 79007
38.0%
property 49682
23.9%
damage 49682
23.9%
injury 29056
 
14.0%
fatal 269
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 207427
14.9%
a 178909
12.8%
128689
 
9.2%
e 99364
 
7.1%
h 79007
 
5.7%
s 79007
 
5.7%
C 79007
 
5.7%
y 78738
 
5.6%
t 49951
 
3.6%
P 49682
 
3.6%
Other values (11) 365172
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1058568
75.9%
Uppercase Letter 207696
 
14.9%
Space Separator 128689
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 207427
19.6%
a 178909
16.9%
e 99364
9.4%
h 79007
 
7.5%
s 79007
 
7.5%
y 78738
 
7.4%
t 49951
 
4.7%
g 49682
 
4.7%
m 49682
 
4.7%
p 49682
 
4.7%
Other values (5) 137119
13.0%
Uppercase Letter
ValueCountFrequency (%)
C 79007
38.0%
P 49682
23.9%
D 49682
23.9%
I 29056
 
14.0%
F 269
 
0.1%
Space Separator
ValueCountFrequency (%)
128689
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1266264
90.8%
Common 128689
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 207427
16.4%
a 178909
14.1%
e 99364
 
7.8%
h 79007
 
6.2%
s 79007
 
6.2%
C 79007
 
6.2%
y 78738
 
6.2%
t 49951
 
3.9%
P 49682
 
3.9%
g 49682
 
3.9%
Other values (10) 315490
24.9%
Common
ValueCountFrequency (%)
128689
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1394953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 207427
14.9%
a 178909
12.8%
128689
 
9.2%
e 99364
 
7.1%
h 79007
 
5.7%
s 79007
 
5.7%
C 79007
 
5.7%
y 78738
 
5.6%
t 49951
 
3.6%
P 49682
 
3.6%
Other values (11) 365172
26.2%
Distinct77471
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size617.4 KiB
Minimum2015-01-01 00:30:00
Maximum2023-12-31 22:15:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-12T01:36:13.776451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:13.973561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Hit/Run
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size154.4 KiB
False
66934 
True
12071 
(Missing)
 
2
ValueCountFrequency (%)
False 66934
84.7%
True 12071
 
15.3%
(Missing) 2
 
< 0.1%
2025-02-12T01:36:14.098535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Route Type
Categorical

Distinct11
Distinct (%)< 0.1%
Missing633
Missing (%)0.8%
Memory size5.2 MiB
Maryland (State)
36299 
County
30307 
Municipality
5366 
US (State)
 
3454
Interstate (State)
 
1541
Other values (6)
 
1407

Length

Max length20
Median length18
Mean length11.583255
Min length2

Characters and Unicode

Total characters907826
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMaryland (State)
2nd rowCounty
3rd rowCounty
4th rowMaryland (State)
5th rowCounty

Common Values

ValueCountFrequency (%)
Maryland (State) 36299
45.9%
County 30307
38.4%
Municipality 5366
 
6.8%
US (State) 3454
 
4.4%
Interstate (State) 1541
 
2.0%
Other Public Roadway 650
 
0.8%
Government 386
 
0.5%
Ramp 339
 
0.4%
Service Road 19
 
< 0.1%
Unknown 12
 
< 0.1%
(Missing) 633
 
0.8%

Length

2025-02-12T01:36:14.238528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
state 41294
34.1%
maryland 36299
30.0%
county 30307
25.0%
municipality 5366
 
4.4%
us 3454
 
2.9%
interstate 1541
 
1.3%
other 650
 
0.5%
public 650
 
0.5%
roadway 650
 
0.5%
government 386
 
0.3%
Other values (5) 390
 
0.3%

Most occurring characters

ValueCountFrequency (%)
t 123920
13.7%
a 122457
13.5%
n 74321
 
8.2%
y 72622
 
8.0%
e 45836
 
5.0%
S 44767
 
4.9%
42613
 
4.7%
l 42315
 
4.7%
M 41665
 
4.6%
( 41294
 
4.5%
Other values (22) 256016
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 658184
72.5%
Uppercase Letter 124441
 
13.7%
Space Separator 42613
 
4.7%
Open Punctuation 41294
 
4.5%
Close Punctuation 41294
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 123920
18.8%
a 122457
18.6%
n 74321
11.3%
y 72622
11.0%
e 45836
 
7.0%
l 42315
 
6.4%
r 38895
 
5.9%
d 36968
 
5.6%
u 36323
 
5.5%
o 31375
 
4.8%
Other values (10) 33152
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 44767
36.0%
M 41665
33.5%
C 30308
24.4%
U 3466
 
2.8%
I 1541
 
1.2%
R 1008
 
0.8%
O 650
 
0.5%
P 650
 
0.5%
G 386
 
0.3%
Space Separator
ValueCountFrequency (%)
42613
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41294
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 782625
86.2%
Common 125201
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 123920
15.8%
a 122457
15.6%
n 74321
9.5%
y 72622
9.3%
e 45836
 
5.9%
S 44767
 
5.7%
l 42315
 
5.4%
M 41665
 
5.3%
r 38895
 
5.0%
d 36968
 
4.7%
Other values (19) 138859
17.7%
Common
ValueCountFrequency (%)
42613
34.0%
( 41294
33.0%
) 41294
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 907826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 123920
13.7%
a 122457
13.5%
n 74321
 
8.2%
y 72622
 
8.0%
e 45836
 
5.0%
S 44767
 
4.9%
42613
 
4.7%
l 42315
 
4.7%
M 41665
 
4.6%
( 41294
 
4.5%
Other values (22) 256016
28.2%

Lane Direction
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.6 MiB
North
23997 
South
22255 
East
16439 
West
15602 
Unknown
 
713

Length

Max length7
Median length5
Mean length4.6124978
Min length4

Characters and Unicode

Total characters364415
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowEast
3rd rowSouth
4th rowWest
5th rowSouth

Common Values

ValueCountFrequency (%)
North 23997
30.4%
South 22255
28.2%
East 16439
20.8%
West 15602
19.7%
Unknown 713
 
0.9%
(Missing) 1
 
< 0.1%

Length

2025-02-12T01:36:14.397064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:14.508291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
north 23997
30.4%
south 22255
28.2%
east 16439
20.8%
west 15602
19.7%
unknown 713
 
0.9%

Most occurring characters

ValueCountFrequency (%)
t 78293
21.5%
o 46965
12.9%
h 46252
12.7%
s 32041
8.8%
N 23997
 
6.6%
r 23997
 
6.6%
S 22255
 
6.1%
u 22255
 
6.1%
E 16439
 
4.5%
a 16439
 
4.5%
Other values (6) 35482
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 285409
78.3%
Uppercase Letter 79006
 
21.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 78293
27.4%
o 46965
16.5%
h 46252
16.2%
s 32041
11.2%
r 23997
 
8.4%
u 22255
 
7.8%
a 16439
 
5.8%
e 15602
 
5.5%
n 2139
 
0.7%
k 713
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 23997
30.4%
S 22255
28.2%
E 16439
20.8%
W 15602
19.7%
U 713
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 364415
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 78293
21.5%
o 46965
12.9%
h 46252
12.7%
s 32041
8.8%
N 23997
 
6.6%
r 23997
 
6.6%
S 22255
 
6.1%
u 22255
 
6.1%
E 16439
 
4.5%
a 16439
 
4.5%
Other values (6) 35482
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 364415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 78293
21.5%
o 46965
12.9%
h 46252
12.7%
s 32041
8.8%
N 23997
 
6.6%
r 23997
 
6.6%
S 22255
 
6.1%
u 22255
 
6.1%
E 16439
 
4.5%
a 16439
 
4.5%
Other values (6) 35482
9.7%

Lane Type
Categorical

Missing 

Distinct14
Distinct (%)0.2%
Missing70174
Missing (%)88.8%
Memory size4.9 MiB
LEFT TURN LANE
2936 
RIGHT TURN LANE
1566 
OTHER
994 
OFF ROAD
764 
SHOULDER AREA
733 
Other values (9)
1840 

Length

Max length20
Median length17
Mean length11.833805
Min length5

Characters and Unicode

Total characters104528
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowUNKNOWN
3rd rowOTHER
4th rowOTHER
5th rowLEFT TURN LANE

Common Values

ValueCountFrequency (%)
LEFT TURN LANE 2936
 
3.7%
RIGHT TURN LANE 1566
 
2.0%
OTHER 994
 
1.3%
OFF ROAD 764
 
1.0%
SHOULDER AREA 733
 
0.9%
ON RAMP 550
 
0.7%
UNKNOWN 376
 
0.5%
CROSSOVER AREA 365
 
0.5%
MEDIAN AREA 262
 
0.3%
ACCELERATION LANE 159
 
0.2%
Other values (4) 128
 
0.2%
(Missing) 70174
88.8%

Length

2025-02-12T01:36:14.670751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lane 4700
22.5%
turn 4502
21.5%
left 2936
14.0%
right 1628
 
7.8%
area 1366
 
6.5%
other 994
 
4.8%
off 764
 
3.7%
road 764
 
3.7%
shoulder 733
 
3.5%
ramp 550
 
2.6%
Other values (11) 1964
9.4%

Most occurring characters

ValueCountFrequency (%)
12068
11.5%
E 11880
11.4%
R 11513
11.0%
N 11340
10.8%
T 10341
9.9%
A 9469
9.1%
L 8567
8.2%
U 5673
5.4%
O 5260
 
5.0%
F 4526
 
4.3%
Other values (12) 13891
13.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 92460
88.5%
Space Separator 12068
 
11.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 11880
12.8%
R 11513
12.5%
N 11340
12.3%
T 10341
11.2%
A 9469
10.2%
L 8567
9.3%
U 5673
6.1%
O 5260
5.7%
F 4526
 
4.9%
H 3355
 
3.6%
Other values (11) 10536
11.4%
Space Separator
ValueCountFrequency (%)
12068
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92460
88.5%
Common 12068
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 11880
12.8%
R 11513
12.5%
N 11340
12.3%
T 10341
11.2%
A 9469
10.2%
L 8567
9.3%
U 5673
6.1%
O 5260
5.7%
F 4526
 
4.9%
H 3355
 
3.6%
Other values (11) 10536
11.4%
Common
ValueCountFrequency (%)
12068
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12068
11.5%
E 11880
11.4%
R 11513
11.0%
N 11340
10.8%
T 10341
9.9%
A 9469
9.1%
L 8567
8.2%
U 5673
5.4%
O 5260
 
5.0%
F 4526
 
4.3%
Other values (12) 13891
13.3%

Number of Lanes
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.3412146
Minimum0
Maximum99
Zeros474
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size617.4 KiB
2025-02-12T01:36:14.813631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum99
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1610166
Coefficient of variation (CV)0.49590352
Kurtosis610.14141
Mean2.3412146
Median Absolute Deviation (MAD)1
Skewness8.1588664
Sum184970
Variance1.3479595
MonotonicityNot monotonic
2025-02-12T01:36:14.952994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 26494
33.5%
3 24985
31.6%
1 18549
23.5%
4 5908
 
7.5%
5 1212
 
1.5%
6 1150
 
1.5%
0 474
 
0.6%
7 146
 
0.2%
8 61
 
0.1%
9 11
 
< 0.1%
Other values (6) 16
 
< 0.1%
ValueCountFrequency (%)
0 474
 
0.6%
1 18549
23.5%
2 26494
33.5%
3 24985
31.6%
4 5908
 
7.5%
5 1212
 
1.5%
6 1150
 
1.5%
7 146
 
0.2%
8 61
 
0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
19 1
 
< 0.1%
13 2
 
< 0.1%
12 5
 
< 0.1%
11 3
 
< 0.1%
10 4
 
< 0.1%
9 11
 
< 0.1%
8 61
 
0.1%
7 146
 
0.2%
6 1150
1.5%

Direction
Categorical

Distinct5
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size4.6 MiB
North
32609 
East
19919 
South
16125 
West
10307 
Unknown
 
35

Length

Max length7
Median length5
Mean length4.6182543
Min length4

Characters and Unicode

Total characters364819
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowEast
3rd rowSouth
4th rowWest
5th rowSouth

Common Values

ValueCountFrequency (%)
North 32609
41.3%
East 19919
25.2%
South 16125
20.4%
West 10307
 
13.0%
Unknown 35
 
< 0.1%
(Missing) 12
 
< 0.1%

Length

2025-02-12T01:36:15.094901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:15.205652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
north 32609
41.3%
east 19919
25.2%
south 16125
20.4%
west 10307
 
13.0%
unknown 35
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 78960
21.6%
o 48769
13.4%
h 48734
13.4%
N 32609
8.9%
r 32609
8.9%
s 30226
 
8.3%
E 19919
 
5.5%
a 19919
 
5.5%
S 16125
 
4.4%
u 16125
 
4.4%
Other values (6) 20824
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 285824
78.3%
Uppercase Letter 78995
 
21.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 78960
27.6%
o 48769
17.1%
h 48734
17.1%
r 32609
11.4%
s 30226
 
10.6%
a 19919
 
7.0%
u 16125
 
5.6%
e 10307
 
3.6%
n 105
 
< 0.1%
k 35
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 32609
41.3%
E 19919
25.2%
S 16125
20.4%
W 10307
 
13.0%
U 35
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 364819
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 78960
21.6%
o 48769
13.4%
h 48734
13.4%
N 32609
8.9%
r 32609
8.9%
s 30226
 
8.3%
E 19919
 
5.5%
a 19919
 
5.5%
S 16125
 
4.4%
u 16125
 
4.4%
Other values (6) 20824
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 364819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 78960
21.6%
o 48769
13.4%
h 48734
13.4%
N 32609
8.9%
r 32609
8.9%
s 30226
 
8.3%
E 19919
 
5.5%
a 19919
 
5.5%
S 16125
 
4.4%
u 16125
 
4.4%
Other values (6) 20824
 
5.7%

Distance
Real number (ℝ)

Zeros 

Distinct423
Distinct (%)0.5%
Missing404
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean62.776744
Minimum0
Maximum1000
Zeros38659
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size617.4 KiB
2025-02-12T01:36:15.377651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q350
95-th percentile350
Maximum1000
Range1000
Interquartile range (IQR)50

Descriptive statistics

Standard deviation135.57481
Coefficient of variation (CV)2.1596343
Kurtosis12.436496
Mean62.776744
Median Absolute Deviation (MAD)0.1
Skewness3.2777034
Sum4934440.4
Variance18380.53
MonotonicityNot monotonic
2025-02-12T01:36:15.567275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38659
48.9%
100 5930
 
7.5%
50 5071
 
6.4%
10 2968
 
3.8%
200 2887
 
3.7%
20 2600
 
3.3%
500 2198
 
2.8%
300 1964
 
2.5%
30 1562
 
2.0%
5 1483
 
1.9%
Other values (413) 13281
 
16.8%
ValueCountFrequency (%)
0 38659
48.9%
0.01 58
 
0.1%
0.02 18
 
< 0.1%
0.03 6
 
< 0.1%
0.04 4
 
< 0.1%
0.05 46
 
0.1%
0.06 3
 
< 0.1%
0.07 2
 
< 0.1%
0.08 6
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
1000 1
 
< 0.1%
999.9 1
 
< 0.1%
999 69
0.1%
997 1
 
< 0.1%
990 4
 
< 0.1%
988 1
 
< 0.1%
986 1
 
< 0.1%
985 2
 
< 0.1%
982 1
 
< 0.1%
975 2
 
< 0.1%

Distance Unit
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size4.6 MiB
FEET
73188 
MILE
 
5380
UNKNOWN
 
437

Length

Max length7
Median length4
Mean length4.0165939
Min length4

Characters and Unicode

Total characters317331
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFEET
2nd rowFEET
3rd rowFEET
4th rowFEET
5th rowFEET

Common Values

ValueCountFrequency (%)
FEET 73188
92.6%
MILE 5380
 
6.8%
UNKNOWN 437
 
0.6%
(Missing) 2
 
< 0.1%

Length

2025-02-12T01:36:15.729483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:15.832921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
feet 73188
92.6%
mile 5380
 
6.8%
unknown 437
 
0.6%

Most occurring characters

ValueCountFrequency (%)
E 151756
47.8%
F 73188
23.1%
T 73188
23.1%
M 5380
 
1.7%
I 5380
 
1.7%
L 5380
 
1.7%
N 1311
 
0.4%
U 437
 
0.1%
K 437
 
0.1%
O 437
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 317331
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 151756
47.8%
F 73188
23.1%
T 73188
23.1%
M 5380
 
1.7%
I 5380
 
1.7%
L 5380
 
1.7%
N 1311
 
0.4%
U 437
 
0.1%
K 437
 
0.1%
O 437
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 317331
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 151756
47.8%
F 73188
23.1%
T 73188
23.1%
M 5380
 
1.7%
I 5380
 
1.7%
L 5380
 
1.7%
N 1311
 
0.4%
U 437
 
0.1%
K 437
 
0.1%
O 437
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 317331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 151756
47.8%
F 73188
23.1%
T 73188
23.1%
M 5380
 
1.7%
I 5380
 
1.7%
L 5380
 
1.7%
N 1311
 
0.4%
U 437
 
0.1%
K 437
 
0.1%
O 437
 
0.1%

Road Grade
Categorical

Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing600
Missing (%)0.8%
Memory size4.8 MiB
LEVEL
59913 
GRADE DOWNHILL
9163 
HILL UPHILL
6779 
HILL CREST
 
2043
OTHER
 
179
Other values (3)
 
330

Length

Max length14
Median length5
Mean length6.7109187
Min length5

Characters and Unicode

Total characters526183
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGRADE DOWNHILL
2nd rowLEVEL
3rd rowLEVEL
4th rowLEVEL
5th rowLEVEL

Common Values

ValueCountFrequency (%)
LEVEL 59913
75.8%
GRADE DOWNHILL 9163
 
11.6%
HILL UPHILL 6779
 
8.6%
HILL CREST 2043
 
2.6%
OTHER 179
 
0.2%
UNKNOWN 142
 
0.2%
DIP SAG 122
 
0.2%
ON BRIDGE 66
 
0.1%
(Missing) 600
 
0.8%

Length

2025-02-12T01:36:15.970914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:16.096709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
level 59913
62.0%
grade 9163
 
9.5%
downhill 9163
 
9.5%
hill 8822
 
9.1%
uphill 6779
 
7.0%
crest 2043
 
2.1%
other 179
 
0.2%
unknown 142
 
0.1%
dip 122
 
0.1%
sag 122
 
0.1%
Other values (2) 132
 
0.1%

Most occurring characters

ValueCountFrequency (%)
L 169354
32.2%
E 131277
24.9%
V 59913
 
11.4%
I 24952
 
4.7%
H 24943
 
4.7%
D 18514
 
3.5%
18173
 
3.5%
R 11451
 
2.2%
N 9655
 
1.8%
O 9550
 
1.8%
Other values (10) 48401
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 508010
96.5%
Space Separator 18173
 
3.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 169354
33.3%
E 131277
25.8%
V 59913
 
11.8%
I 24952
 
4.9%
H 24943
 
4.9%
D 18514
 
3.6%
R 11451
 
2.3%
N 9655
 
1.9%
O 9550
 
1.9%
G 9351
 
1.8%
Other values (9) 39050
 
7.7%
Space Separator
ValueCountFrequency (%)
18173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 508010
96.5%
Common 18173
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 169354
33.3%
E 131277
25.8%
V 59913
 
11.8%
I 24952
 
4.9%
H 24943
 
4.9%
D 18514
 
3.6%
R 11451
 
2.3%
N 9655
 
1.9%
O 9550
 
1.9%
G 9351
 
1.8%
Other values (9) 39050
 
7.7%
Common
ValueCountFrequency (%)
18173
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 526183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 169354
32.2%
E 131277
24.9%
V 59913
 
11.4%
I 24952
 
4.7%
H 24943
 
4.7%
D 18514
 
3.5%
18173
 
3.5%
R 11451
 
2.2%
N 9655
 
1.8%
O 9550
 
1.8%
Other values (10) 48401
 
9.2%
Distinct3707
Distinct (%)4.7%
Missing2
Missing (%)< 0.1%
Memory size5.3 MiB
2025-02-12T01:36:16.437835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length38
Mean length13.139523
Min length4

Characters and Unicode

Total characters1038088
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1722 ?
Unique (%)2.2%

Sample

1st rowNORBECK RD
2nd rowTHORNAPPLE ST
3rd rowVALLEY BEND DR
4th rowUNIVERSITY BLVD W
5th rowROCKVILLE PIKE
ValueCountFrequency (%)
rd 34524
 
18.2%
ave 17894
 
9.4%
dr 6330
 
3.3%
georgia 5010
 
2.6%
blvd 4521
 
2.4%
pike 4377
 
2.3%
mill 3332
 
1.8%
new 3183
 
1.7%
hampshire 3179
 
1.7%
frederick 2991
 
1.6%
Other values (2989) 104473
55.0%
2025-02-12T01:36:16.967856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
110825
 
10.7%
E 107949
 
10.4%
R 107865
 
10.4%
D 72729
 
7.0%
A 69205
 
6.7%
I 59652
 
5.7%
L 57991
 
5.6%
O 55798
 
5.4%
N 48424
 
4.7%
S 38355
 
3.7%
Other values (34) 309295
29.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 924056
89.0%
Space Separator 110825
 
10.7%
Decimal Number 2849
 
0.3%
Other Punctuation 124
 
< 0.1%
Close Punctuation 114
 
< 0.1%
Open Punctuation 114
 
< 0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 107949
11.7%
R 107865
11.7%
D 72729
 
7.9%
A 69205
 
7.5%
I 59652
 
6.5%
L 57991
 
6.3%
O 55798
 
6.0%
N 48424
 
5.2%
S 38355
 
4.2%
V 36743
 
4.0%
Other values (16) 269345
29.1%
Decimal Number
ValueCountFrequency (%)
1 588
20.6%
6 367
12.9%
2 359
12.6%
0 347
12.2%
7 339
11.9%
9 210
 
7.4%
5 200
 
7.0%
4 177
 
6.2%
3 155
 
5.4%
8 107
 
3.8%
Other Punctuation
ValueCountFrequency (%)
# 61
49.2%
/ 48
38.7%
. 14
 
11.3%
& 1
 
0.8%
Space Separator
ValueCountFrequency (%)
110825
100.0%
Close Punctuation
ValueCountFrequency (%)
) 114
100.0%
Open Punctuation
ValueCountFrequency (%)
( 114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 924056
89.0%
Common 114032
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 107949
11.7%
R 107865
11.7%
D 72729
 
7.9%
A 69205
 
7.5%
I 59652
 
6.5%
L 57991
 
6.3%
O 55798
 
6.0%
N 48424
 
5.2%
S 38355
 
4.2%
V 36743
 
4.0%
Other values (16) 269345
29.1%
Common
ValueCountFrequency (%)
110825
97.2%
1 588
 
0.5%
6 367
 
0.3%
2 359
 
0.3%
0 347
 
0.3%
7 339
 
0.3%
9 210
 
0.2%
5 200
 
0.2%
4 177
 
0.2%
3 155
 
0.1%
Other values (8) 465
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1038088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110825
 
10.7%
E 107949
 
10.4%
R 107865
 
10.4%
D 72729
 
7.0%
A 69205
 
6.7%
I 59652
 
5.7%
L 57991
 
5.6%
O 55798
 
5.4%
N 48424
 
4.7%
S 38355
 
3.7%
Other values (34) 309295
29.8%
Distinct6574
Distinct (%)8.3%
Missing12
Missing (%)< 0.1%
Memory size5.3 MiB
2025-02-12T01:36:17.270400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length38
Mean length13.496171
Min length3

Characters and Unicode

Total characters1066130
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2350 ?
Unique (%)3.0%

Sample

1st rowWINTERGATE DR
2nd rowLENHART DR
3rd rowCROSS LAUREL DR
4th rowELKIN ST
5th rowPARK RD
ValueCountFrequency (%)
rd 25207
 
11.9%
dr 13634
 
6.4%
ave 11936
 
5.6%
st 5308
 
2.5%
to 5283
 
2.5%
la 4367
 
2.1%
fr 3393
 
1.6%
blvd 3328
 
1.6%
ramp 3204
 
1.5%
md 2309
 
1.1%
Other values (4544) 133607
63.1%
2025-02-12T01:36:17.765153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
132628
12.4%
R 107993
 
10.1%
E 94426
 
8.9%
D 72905
 
6.8%
A 72151
 
6.8%
O 57545
 
5.4%
L 56277
 
5.3%
N 52659
 
4.9%
T 48134
 
4.5%
S 47176
 
4.4%
Other values (38) 324236
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 908790
85.2%
Space Separator 132628
 
12.4%
Decimal Number 20760
 
1.9%
Other Punctuation 1742
 
0.2%
Open Punctuation 1068
 
0.1%
Close Punctuation 1068
 
0.1%
Dash Punctuation 74
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 107993
11.9%
E 94426
 
10.4%
D 72905
 
8.0%
A 72151
 
7.9%
O 57545
 
6.3%
L 56277
 
6.2%
N 52659
 
5.8%
T 48134
 
5.3%
S 47176
 
5.2%
I 46382
 
5.1%
Other values (16) 253142
27.9%
Decimal Number
ValueCountFrequency (%)
2 2949
14.2%
1 2883
13.9%
0 2682
12.9%
5 2492
12.0%
7 2302
11.1%
9 2103
10.1%
4 1951
9.4%
6 1179
 
5.7%
8 1157
 
5.6%
3 1062
 
5.1%
Other Punctuation
ValueCountFrequency (%)
# 951
54.6%
/ 427
24.5%
. 260
 
14.9%
& 67
 
3.8%
' 26
 
1.5%
: 7
 
0.4%
" 2
 
0.1%
, 2
 
0.1%
Space Separator
ValueCountFrequency (%)
132628
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1068
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1068
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 908790
85.2%
Common 157340
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 107993
11.9%
E 94426
 
10.4%
D 72905
 
8.0%
A 72151
 
7.9%
O 57545
 
6.3%
L 56277
 
6.2%
N 52659
 
5.8%
T 48134
 
5.3%
S 47176
 
5.2%
I 46382
 
5.1%
Other values (16) 253142
27.9%
Common
ValueCountFrequency (%)
132628
84.3%
2 2949
 
1.9%
1 2883
 
1.8%
0 2682
 
1.7%
5 2492
 
1.6%
7 2302
 
1.5%
9 2103
 
1.3%
4 1951
 
1.2%
6 1179
 
0.7%
8 1157
 
0.7%
Other values (12) 5014
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1066130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
132628
12.4%
R 107993
 
10.1%
E 94426
 
8.9%
D 72905
 
6.8%
A 72151
 
6.8%
O 57545
 
5.4%
L 56277
 
5.3%
N 52659
 
4.9%
T 48134
 
4.5%
S 47176
 
4.4%
Other values (38) 324236
30.4%

Off-Road Description
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing79006
Missing (%)> 99.9%
Memory size2.4 MiB
2025-02-12T01:36:17.916015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters32
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowParking Lot of 10401 Fernwood Rd
ValueCountFrequency (%)
parking 1
16.7%
lot 1
16.7%
of 1
16.7%
10401 1
16.7%
fernwood 1
16.7%
rd 1
16.7%
2025-02-12T01:36:18.220230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
15.6%
o 4
 
12.5%
1 2
 
6.2%
r 2
 
6.2%
d 2
 
6.2%
n 2
 
6.2%
0 2
 
6.2%
P 1
 
3.1%
w 1
 
3.1%
e 1
 
3.1%
Other values (10) 10
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18
56.2%
Space Separator 5
 
15.6%
Decimal Number 5
 
15.6%
Uppercase Letter 4
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4
22.2%
r 2
11.1%
d 2
11.1%
n 2
11.1%
w 1
 
5.6%
e 1
 
5.6%
t 1
 
5.6%
f 1
 
5.6%
a 1
 
5.6%
g 1
 
5.6%
Other values (2) 2
11.1%
Uppercase Letter
ValueCountFrequency (%)
P 1
25.0%
F 1
25.0%
L 1
25.0%
R 1
25.0%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
0 2
40.0%
4 1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22
68.8%
Common 10
31.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4
18.2%
r 2
 
9.1%
d 2
 
9.1%
n 2
 
9.1%
P 1
 
4.5%
w 1
 
4.5%
e 1
 
4.5%
F 1
 
4.5%
t 1
 
4.5%
f 1
 
4.5%
Other values (6) 6
27.3%
Common
ValueCountFrequency (%)
5
50.0%
1 2
 
20.0%
0 2
 
20.0%
4 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5
15.6%
o 4
 
12.5%
1 2
 
6.2%
r 2
 
6.2%
d 2
 
6.2%
n 2
 
6.2%
0 2
 
6.2%
P 1
 
3.1%
w 1
 
3.1%
e 1
 
3.1%
Other values (10) 10
31.2%

Municipality
Categorical

High correlation  Imbalance  Missing 

Distinct20
Distinct (%)0.2%
Missing69268
Missing (%)87.7%
Memory size4.9 MiB
ROCKVILLE
4726 
GAITHERSBURG
3233 
TAKOMA PARK
905 
KENSINGTON
 
203
CHEVY CHASE #4
 
163
Other values (15)
509 

Length

Max length19
Median length18
Mean length10.554985
Min length8

Characters and Unicode

Total characters102795
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowROCKVILLE
2nd rowROCKVILLE
3rd rowROCKVILLE
4th rowGAITHERSBURG
5th rowROCKVILLE

Common Values

ValueCountFrequency (%)
ROCKVILLE 4726
 
6.0%
GAITHERSBURG 3233
 
4.1%
TAKOMA PARK 905
 
1.1%
KENSINGTON 203
 
0.3%
CHEVY CHASE #4 163
 
0.2%
CHEVY CHASE #3 78
 
0.1%
FRIENDSHIP HEIGHTS 74
 
0.1%
POOLESVILLE 69
 
0.1%
CHEVY CHASE VIEW 47
 
0.1%
CHEVY CHASE VILLAGE 41
 
0.1%
Other values (10) 200
 
0.3%
(Missing) 69268
87.7%

Length

2025-02-12T01:36:18.345170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rockville 4726
40.9%
gaithersburg 3233
28.0%
park 934
 
8.1%
takoma 905
 
7.8%
chevy 378
 
3.3%
chase 378
 
3.3%
kensington 203
 
1.8%
4 163
 
1.4%
3 78
 
0.7%
friendship 74
 
0.6%
Other values (17) 489
 
4.2%

Most occurring characters

ValueCountFrequency (%)
R 12345
12.0%
L 9826
 
9.6%
E 9479
 
9.2%
I 8648
 
8.4%
G 6875
 
6.7%
K 6780
 
6.6%
A 6501
 
6.3%
O 6144
 
6.0%
C 5498
 
5.3%
V 5311
 
5.2%
Other values (17) 25388
24.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 100423
97.7%
Space Separator 1822
 
1.8%
Other Punctuation 275
 
0.3%
Decimal Number 275
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 12345
12.3%
L 9826
9.8%
E 9479
9.4%
I 8648
 
8.6%
G 6875
 
6.8%
K 6780
 
6.8%
A 6501
 
6.5%
O 6144
 
6.1%
C 5498
 
5.5%
V 5311
 
5.3%
Other values (12) 23016
22.9%
Decimal Number
ValueCountFrequency (%)
4 163
59.3%
3 78
28.4%
5 34
 
12.4%
Space Separator
ValueCountFrequency (%)
1822
100.0%
Other Punctuation
ValueCountFrequency (%)
# 275
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100423
97.7%
Common 2372
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 12345
12.3%
L 9826
9.8%
E 9479
9.4%
I 8648
 
8.6%
G 6875
 
6.8%
K 6780
 
6.8%
A 6501
 
6.5%
O 6144
 
6.1%
C 5498
 
5.5%
V 5311
 
5.3%
Other values (12) 23016
22.9%
Common
ValueCountFrequency (%)
1822
76.8%
# 275
 
11.6%
4 163
 
6.9%
3 78
 
3.3%
5 34
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 12345
12.0%
L 9826
 
9.6%
E 9479
 
9.2%
I 8648
 
8.4%
G 6875
 
6.7%
K 6780
 
6.6%
A 6501
 
6.3%
O 6144
 
6.0%
C 5498
 
5.3%
V 5311
 
5.2%
Other values (17) 25388
24.7%

Related Non-Motorist
Categorical

High correlation  Imbalance  Missing 

Distinct11
Distinct (%)0.3%
Missing75134
Missing (%)95.1%
Memory size4.8 MiB
PEDESTRIAN
2636 
BICYCLIST
973 
OTHER
 
145
OTHER CONVEYANCE
 
60
MACHINE OPERATOR/RIDER
 
24
Other values (6)
 
35

Length

Max length28
Median length10
Mean length9.8068681
Min length5

Characters and Unicode

Total characters37982
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPEDESTRIAN
2nd rowPEDESTRIAN
3rd rowPEDESTRIAN
4th rowPEDESTRIAN
5th rowOTHER CONVEYANCE

Common Values

ValueCountFrequency (%)
PEDESTRIAN 2636
 
3.3%
BICYCLIST 973
 
1.2%
OTHER 145
 
0.2%
OTHER CONVEYANCE 60
 
0.1%
MACHINE OPERATOR/RIDER 24
 
< 0.1%
OTHER PEDALCYCLIST 20
 
< 0.1%
OTHER, PEDESTRIAN 5
 
< 0.1%
BICYCLIST, PEDESTRIAN 4
 
< 0.1%
BICYCLIST, OTHER 3
 
< 0.1%
OTHER CONVEYANCE, PEDESTRIAN 2
 
< 0.1%
(Missing) 75134
95.1%

Length

2025-02-12T01:36:18.490054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pedestrian 2647
66.3%
bicyclist 980
 
24.5%
other 235
 
5.9%
conveyance 62
 
1.6%
machine 24
 
0.6%
operator/rider 24
 
0.6%
pedalcyclist 20
 
0.5%
in 1
 
< 0.1%
animal-drawn 1
 
< 0.1%
veh 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 5746
15.1%
I 4677
12.3%
T 3906
10.3%
S 3647
9.6%
R 2979
7.8%
N 2798
7.4%
A 2780
7.3%
D 2692
7.1%
P 2691
7.1%
C 2148
 
5.7%
Other values (12) 3918
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 37821
99.6%
Space Separator 122
 
0.3%
Other Punctuation 38
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 5746
15.2%
I 4677
12.4%
T 3906
10.3%
S 3647
9.6%
R 2979
7.9%
N 2798
7.4%
A 2780
7.4%
D 2692
7.1%
P 2691
7.1%
C 2148
 
5.7%
Other values (8) 3757
9.9%
Other Punctuation
ValueCountFrequency (%)
/ 24
63.2%
, 14
36.8%
Space Separator
ValueCountFrequency (%)
122
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37821
99.6%
Common 161
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 5746
15.2%
I 4677
12.4%
T 3906
10.3%
S 3647
9.6%
R 2979
7.9%
N 2798
7.4%
A 2780
7.4%
D 2692
7.1%
P 2691
7.1%
C 2148
 
5.7%
Other values (8) 3757
9.9%
Common
ValueCountFrequency (%)
122
75.8%
/ 24
 
14.9%
, 14
 
8.7%
- 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 5746
15.1%
I 4677
12.3%
T 3906
10.3%
S 3647
9.6%
R 2979
7.8%
N 2798
7.4%
A 2780
7.3%
D 2692
7.1%
P 2691
7.1%
C 2148
 
5.7%
Other values (12) 3918
10.3%

At Fault
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.8 MiB
DRIVER
73574 
UNKNOWN
 
4248
NONMOTORIST
 
1063
BOTH
 
121

Length

Max length11
Median length6
Mean length6.1179784
Min length4

Characters and Unicode

Total characters483357
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRIVER
2nd rowDRIVER
3rd rowUNKNOWN
4th rowDRIVER
5th rowDRIVER

Common Values

ValueCountFrequency (%)
DRIVER 73574
93.1%
UNKNOWN 4248
 
5.4%
NONMOTORIST 1063
 
1.3%
BOTH 121
 
0.2%
(Missing) 1
 
< 0.1%

Length

2025-02-12T01:36:18.633666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:18.738996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
driver 73574
93.1%
unknown 4248
 
5.4%
nonmotorist 1063
 
1.3%
both 121
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R 148211
30.7%
I 74637
15.4%
D 73574
15.2%
V 73574
15.2%
E 73574
15.2%
N 14870
 
3.1%
O 7558
 
1.6%
U 4248
 
0.9%
K 4248
 
0.9%
W 4248
 
0.9%
Other values (5) 4615
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 483357
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 148211
30.7%
I 74637
15.4%
D 73574
15.2%
V 73574
15.2%
E 73574
15.2%
N 14870
 
3.1%
O 7558
 
1.6%
U 4248
 
0.9%
K 4248
 
0.9%
W 4248
 
0.9%
Other values (5) 4615
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 483357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 148211
30.7%
I 74637
15.4%
D 73574
15.2%
V 73574
15.2%
E 73574
15.2%
N 14870
 
3.1%
O 7558
 
1.6%
U 4248
 
0.9%
K 4248
 
0.9%
W 4248
 
0.9%
Other values (5) 4615
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 148211
30.7%
I 74637
15.4%
D 73574
15.2%
V 73574
15.2%
E 73574
15.2%
N 14870
 
3.1%
O 7558
 
1.6%
U 4248
 
0.9%
K 4248
 
0.9%
W 4248
 
0.9%
Other values (5) 4615
 
1.0%

Collision Type
Categorical

Distinct18
Distinct (%)< 0.1%
Missing311
Missing (%)0.4%
Memory size5.6 MiB
SAME DIR REAR END
23186 
STRAIGHT MOVEMENT ANGLE
13116 
SINGLE VEHICLE
12515 
SAME DIRECTION SIDESWIPE
7505 
OTHER
7474 
Other values (13)
14900 

Length

Max length28
Median length27
Mean length17.488703
Min length5

Characters and Unicode

Total characters1376291
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSAME DIR REAR END
2nd rowOTHER
3rd rowOPPOSITE DIRECTION SIDESWIPE
4th rowSINGLE VEHICLE
5th rowSAME DIRECTION SIDESWIPE

Common Values

ValueCountFrequency (%)
SAME DIR REAR END 23186
29.3%
STRAIGHT MOVEMENT ANGLE 13116
16.6%
SINGLE VEHICLE 12515
15.8%
SAME DIRECTION SIDESWIPE 7505
 
9.5%
OTHER 7474
 
9.5%
HEAD ON LEFT TURN 5762
 
7.3%
SAME DIRECTION RIGHT TURN 1705
 
2.2%
HEAD ON 1671
 
2.1%
SAME DIRECTION LEFT TURN 1617
 
2.0%
OPPOSITE DIRECTION SIDESWIPE 1134
 
1.4%
Other values (8) 3011
 
3.8%

Length

2025-02-12T01:36:19.164916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
same 34976
14.4%
dir 24278
10.0%
rear 23186
 
9.5%
end 23186
 
9.5%
angle 14837
 
6.1%
straight 13116
 
5.4%
movement 13116
 
5.4%
single 12515
 
5.1%
vehicle 12515
 
5.1%
direction 11961
 
4.9%
Other values (12) 59350
24.4%

Most occurring characters

ValueCountFrequency (%)
E 219101
15.9%
164340
11.9%
R 118007
8.6%
I 107449
 
7.8%
N 96189
 
7.0%
A 93851
 
6.8%
T 85700
 
6.2%
S 80869
 
5.9%
D 76450
 
5.6%
M 62929
 
4.6%
Other values (12) 271406
19.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1211951
88.1%
Space Separator 164340
 
11.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 219101
18.1%
R 118007
9.7%
I 107449
8.9%
N 96189
7.9%
A 93851
7.7%
T 85700
 
7.1%
S 80869
 
6.7%
D 76450
 
6.3%
M 62929
 
5.2%
L 49202
 
4.1%
Other values (11) 222204
18.3%
Space Separator
ValueCountFrequency (%)
164340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1211951
88.1%
Common 164340
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 219101
18.1%
R 118007
9.7%
I 107449
8.9%
N 96189
7.9%
A 93851
7.7%
T 85700
 
7.1%
S 80869
 
6.7%
D 76450
 
6.3%
M 62929
 
5.2%
L 49202
 
4.1%
Other values (11) 222204
18.3%
Common
ValueCountFrequency (%)
164340
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1376291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 219101
15.9%
164340
11.9%
R 118007
8.6%
I 107449
 
7.8%
N 96189
 
7.0%
A 93851
 
6.8%
T 85700
 
6.2%
S 80869
 
5.9%
D 76450
 
5.6%
M 62929
 
4.6%
Other values (12) 271406
19.7%

Weather
Categorical

Imbalance  Missing 

Distinct12
Distinct (%)< 0.1%
Missing6146
Missing (%)7.8%
Memory size4.7 MiB
CLEAR
52999 
RAINING
9953 
CLOUDY
7839 
SNOW
 
725
FOGGY
 
381
Other values (7)
 
964

Length

Max length24
Median length5
Mean length5.4095195
Min length4

Characters and Unicode

Total characters394143
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLOUDY
2nd rowCLEAR
3rd rowCLEAR
4th rowCLOUDY
5th rowCLEAR

Common Values

ValueCountFrequency (%)
CLEAR 52999
67.1%
RAINING 9953
 
12.6%
CLOUDY 7839
 
9.9%
SNOW 725
 
0.9%
FOGGY 381
 
0.5%
UNKNOWN 305
 
0.4%
WINTRY MIX 219
 
0.3%
OTHER 179
 
0.2%
SLEET 114
 
0.1%
SEVERE WINDS 81
 
0.1%
Other values (2) 66
 
0.1%
(Missing) 6146
 
7.8%

Length

2025-02-12T01:36:19.325381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear 52999
72.4%
raining 9953
 
13.6%
cloudy 7839
 
10.7%
snow 784
 
1.1%
foggy 381
 
0.5%
unknown 305
 
0.4%
wintry 219
 
0.3%
mix 219
 
0.3%
other 179
 
0.2%
sleet 114
 
0.2%
Other values (6) 249
 
0.3%

Most occurring characters

ValueCountFrequency (%)
R 63438
16.1%
A 62959
16.0%
L 61025
15.5%
C 60838
15.4%
E 53649
13.6%
N 21978
 
5.6%
I 20505
 
5.2%
G 10781
 
2.7%
O 9561
 
2.4%
Y 8439
 
2.1%
Other values (14) 20970
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 393749
99.9%
Space Separator 380
 
0.1%
Other Punctuation 14
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 63438
16.1%
A 62959
16.0%
L 61025
15.5%
C 60838
15.5%
E 53649
13.6%
N 21978
 
5.6%
I 20505
 
5.2%
G 10781
 
2.7%
O 9561
 
2.4%
Y 8439
 
2.1%
Other values (12) 20576
 
5.2%
Space Separator
ValueCountFrequency (%)
380
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 393749
99.9%
Common 394
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 63438
16.1%
A 62959
16.0%
L 61025
15.5%
C 60838
15.5%
E 53649
13.6%
N 21978
 
5.6%
I 20505
 
5.2%
G 10781
 
2.7%
O 9561
 
2.4%
Y 8439
 
2.1%
Other values (12) 20576
 
5.2%
Common
ValueCountFrequency (%)
380
96.4%
, 14
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394143
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 63438
16.1%
A 62959
16.0%
L 61025
15.5%
C 60838
15.4%
E 53649
13.6%
N 21978
 
5.6%
I 20505
 
5.2%
G 10781
 
2.7%
O 9561
 
2.4%
Y 8439
 
2.1%
Other values (14) 20970
 
5.3%

Surface Condition
Categorical

Imbalance  Missing 

Distinct11
Distinct (%)< 0.1%
Missing2175
Missing (%)2.8%
Memory size4.5 MiB
DRY
60311 
WET
14671 
ICE
 
618
SNOW
 
557
UNKNOWN
 
375
Other values (6)
 
300

Length

Max length22
Median length3
Mean length3.045632
Min length3

Characters and Unicode

Total characters234002
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRY
2nd rowDRY
3rd rowDRY
4th rowDRY
5th rowDRY

Common Values

ValueCountFrequency (%)
DRY 60311
76.3%
WET 14671
 
18.6%
ICE 618
 
0.8%
SNOW 557
 
0.7%
UNKNOWN 375
 
0.5%
SLUSH 128
 
0.2%
OTHER 91
 
0.1%
MUD, DIRT, GRAVEL 34
 
< 0.1%
WATER(STANDING/MOVING) 28
 
< 0.1%
OIL 16
 
< 0.1%
(Missing) 2175
 
2.8%

Length

2025-02-12T01:36:19.492917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dry 60311
78.4%
wet 14671
 
19.1%
ice 618
 
0.8%
snow 557
 
0.7%
unknown 375
 
0.5%
slush 128
 
0.2%
other 91
 
0.1%
mud 34
 
< 0.1%
dirt 34
 
< 0.1%
gravel 34
 
< 0.1%
Other values (3) 47
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R 60498
25.9%
D 60410
25.8%
Y 60311
25.8%
W 15631
 
6.7%
E 15442
 
6.6%
T 14852
 
6.3%
N 1769
 
0.8%
O 1067
 
0.5%
S 844
 
0.4%
I 724
 
0.3%
Other values (14) 2454
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 233782
99.9%
Other Punctuation 96
 
< 0.1%
Space Separator 68
 
< 0.1%
Open Punctuation 28
 
< 0.1%
Close Punctuation 28
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 60498
25.9%
D 60410
25.8%
Y 60311
25.8%
W 15631
 
6.7%
E 15442
 
6.6%
T 14852
 
6.4%
N 1769
 
0.8%
O 1067
 
0.5%
S 844
 
0.4%
I 724
 
0.3%
Other values (9) 2234
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 68
70.8%
/ 28
29.2%
Space Separator
ValueCountFrequency (%)
68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 233782
99.9%
Common 220
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 60498
25.9%
D 60410
25.8%
Y 60311
25.8%
W 15631
 
6.7%
E 15442
 
6.6%
T 14852
 
6.4%
N 1769
 
0.8%
O 1067
 
0.5%
S 844
 
0.4%
I 724
 
0.3%
Other values (9) 2234
 
1.0%
Common
ValueCountFrequency (%)
, 68
30.9%
68
30.9%
( 28
12.7%
/ 28
12.7%
) 28
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 60498
25.9%
D 60410
25.8%
Y 60311
25.8%
W 15631
 
6.7%
E 15442
 
6.6%
T 14852
 
6.3%
N 1769
 
0.8%
O 1067
 
0.5%
S 844
 
0.4%
I 724
 
0.3%
Other values (14) 2454
 
1.0%

Light
Categorical

Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing604
Missing (%)0.8%
Memory size5.0 MiB
DAYLIGHT
50708 
DARK LIGHTS ON
19871 
DARK NO LIGHTS
 
3031
DUSK
 
1771
DAWN
 
1710
Other values (3)
 
1312

Length

Max length24
Median length8
Mean length9.7480836
Min length4

Characters and Unicode

Total characters764279
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDAYLIGHT
2nd rowDAYLIGHT
3rd rowDARK LIGHTS ON
4th rowDAYLIGHT
5th rowDAYLIGHT

Common Values

ValueCountFrequency (%)
DAYLIGHT 50708
64.2%
DARK LIGHTS ON 19871
 
25.2%
DARK NO LIGHTS 3031
 
3.8%
DUSK 1771
 
2.2%
DAWN 1710
 
2.2%
DARK -- UNKNOWN LIGHTING 889
 
1.1%
UNKNOWN 306
 
0.4%
OTHER 117
 
0.1%
(Missing) 604
 
0.8%

Length

2025-02-12T01:36:19.653944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:19.791620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
daylight 50708
40.0%
dark 23791
18.8%
lights 22902
18.1%
on 19871
 
15.7%
no 3031
 
2.4%
dusk 1771
 
1.4%
dawn 1710
 
1.3%
unknown 1195
 
0.9%
889
 
0.7%
lighting 889
 
0.7%

Most occurring characters

ValueCountFrequency (%)
D 77980
10.2%
A 76209
10.0%
I 75388
9.9%
G 75388
9.9%
H 74616
9.8%
T 74616
9.8%
L 74499
9.7%
Y 50708
6.6%
48471
6.3%
N 29086
 
3.8%
Other values (8) 107318
14.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 714030
93.4%
Space Separator 48471
 
6.3%
Dash Punctuation 1778
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 77980
10.9%
A 76209
10.7%
I 75388
10.6%
G 75388
10.6%
H 74616
10.4%
T 74616
10.4%
L 74499
10.4%
Y 50708
7.1%
N 29086
 
4.1%
K 26757
 
3.7%
Other values (6) 78783
11.0%
Space Separator
ValueCountFrequency (%)
48471
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1778
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 714030
93.4%
Common 50249
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 77980
10.9%
A 76209
10.7%
I 75388
10.6%
G 75388
10.6%
H 74616
10.4%
T 74616
10.4%
L 74499
10.4%
Y 50708
7.1%
N 29086
 
4.1%
K 26757
 
3.7%
Other values (6) 78783
11.0%
Common
ValueCountFrequency (%)
48471
96.5%
- 1778
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 764279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 77980
10.2%
A 76209
10.0%
I 75388
9.9%
G 75388
9.9%
H 74616
9.8%
T 74616
9.8%
L 74499
9.7%
Y 50708
6.6%
48471
6.3%
N 29086
 
3.8%
Other values (8) 107318
14.0%

Traffic Control
Categorical

Imbalance  Missing 

Distinct11
Distinct (%)< 0.1%
Missing10846
Missing (%)13.7%
Memory size5.2 MiB
NO CONTROLS
32194 
TRAFFIC SIGNAL
26799 
STOP SIGN
5892 
FLASHING TRAFFIC SIGNAL
 
1051
OTHER
 
956
Other values (6)
 
1269

Length

Max length23
Median length14
Mean length12.083684
Min length5

Characters and Unicode

Total characters823636
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO CONTROLS
2nd rowNO CONTROLS
3rd rowNO CONTROLS
4th rowNO CONTROLS
5th rowNO CONTROLS

Common Values

ValueCountFrequency (%)
NO CONTROLS 32194
40.7%
TRAFFIC SIGNAL 26799
33.9%
STOP SIGN 5892
 
7.5%
FLASHING TRAFFIC SIGNAL 1051
 
1.3%
OTHER 956
 
1.2%
YIELD SIGN 833
 
1.1%
UNKNOWN 155
 
0.2%
PERSON 145
 
0.2%
WARNING SIGN 98
 
0.1%
RAILWAY CROSSING DEVICE 25
 
< 0.1%
(Missing) 10846
 
13.7%

Length

2025-02-12T01:36:20.002646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 32194
23.6%
controls 32194
23.6%
traffic 27850
20.5%
signal 27850
20.5%
sign 6836
 
5.0%
stop 5892
 
4.3%
flashing 1051
 
0.8%
other 956
 
0.7%
yield 833
 
0.6%
unknown 155
 
0.1%
Other values (7) 357
 
0.3%

Most occurring characters

ValueCountFrequency (%)
O 103794
12.6%
N 100969
12.3%
S 74031
9.0%
68007
8.3%
T 66892
8.1%
I 64606
7.8%
L 61966
7.5%
R 61293
7.4%
C 60120
7.3%
A 56899
6.9%
Other values (12) 105059
12.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 755629
91.7%
Space Separator 68007
 
8.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 103794
13.7%
N 100969
13.4%
S 74031
9.8%
T 66892
8.9%
I 64606
8.5%
L 61966
8.2%
R 61293
8.1%
C 60120
8.0%
A 56899
7.5%
F 56751
7.5%
Other values (11) 48308
6.4%
Space Separator
ValueCountFrequency (%)
68007
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 755629
91.7%
Common 68007
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 103794
13.7%
N 100969
13.4%
S 74031
9.8%
T 66892
8.9%
I 64606
8.5%
L 61966
8.2%
R 61293
8.1%
C 60120
8.0%
A 56899
7.5%
F 56751
7.5%
Other values (11) 48308
6.4%
Common
ValueCountFrequency (%)
68007
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 823636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 103794
12.6%
N 100969
12.3%
S 74031
9.0%
68007
8.3%
T 66892
8.1%
I 64606
7.8%
L 61966
7.5%
R 61293
7.4%
C 60120
7.3%
A 56899
6.9%
Other values (12) 105059
12.8%

Driver Substance Abuse
Categorical

High correlation  Imbalance  Missing 

Distinct49
Distinct (%)0.1%
Missing12130
Missing (%)15.4%
Memory size5.3 MiB
NONE DETECTED
51065 
NONE DETECTED, UNKNOWN
 
4474
UNKNOWN
 
2634
N/A, NONE DETECTED
 
2627
ALCOHOL PRESENT
 
1696
Other values (44)
 
4381

Length

Max length46
Median length13
Mean length14.307819
Min length5

Characters and Unicode

Total characters956864
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowNONE DETECTED
2nd rowUNKNOWN
3rd rowNONE DETECTED
4th rowNONE DETECTED
5th rowNONE DETECTED

Common Values

ValueCountFrequency (%)
NONE DETECTED 51065
64.6%
NONE DETECTED, UNKNOWN 4474
 
5.7%
UNKNOWN 2634
 
3.3%
N/A, NONE DETECTED 2627
 
3.3%
ALCOHOL PRESENT 1696
 
2.1%
ALCOHOL PRESENT, NONE DETECTED 1262
 
1.6%
N/A, UNKNOWN 893
 
1.1%
ALCOHOL CONTRIBUTED 684
 
0.9%
ALCOHOL CONTRIBUTED, NONE DETECTED 400
 
0.5%
ALCOHOL PRESENT, N/A 362
 
0.5%
Other values (39) 780
 
1.0%
(Missing) 12130
 
15.4%

Length

2025-02-12T01:36:20.186925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 60116
42.0%
detected 60116
42.0%
unknown 8045
 
5.6%
alcohol 4557
 
3.2%
n/a 4119
 
2.9%
present 3739
 
2.6%
contributed 1382
 
1.0%
illegal 299
 
0.2%
drug 299
 
0.2%
medication 144
 
0.1%
Other values (4) 247
 
0.2%

Most occurring characters

ValueCountFrequency (%)
E 249973
26.1%
N 153993
16.1%
T 127046
13.3%
D 122137
12.8%
O 79009
 
8.3%
76186
 
8.0%
C 66400
 
6.9%
, 10570
 
1.1%
L 10011
 
1.0%
U 9806
 
1.0%
Other values (12) 51733
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 865989
90.5%
Space Separator 76186
 
8.0%
Other Punctuation 14689
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 249973
28.9%
N 153993
17.8%
T 127046
14.7%
D 122137
14.1%
O 79009
 
9.1%
C 66400
 
7.7%
L 10011
 
1.2%
U 9806
 
1.1%
A 9240
 
1.1%
K 8045
 
0.9%
Other values (9) 30329
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 10570
72.0%
/ 4119
 
28.0%
Space Separator
ValueCountFrequency (%)
76186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 865989
90.5%
Common 90875
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 249973
28.9%
N 153993
17.8%
T 127046
14.7%
D 122137
14.1%
O 79009
 
9.1%
C 66400
 
7.7%
L 10011
 
1.2%
U 9806
 
1.1%
A 9240
 
1.1%
K 8045
 
0.9%
Other values (9) 30329
 
3.5%
Common
ValueCountFrequency (%)
76186
83.8%
, 10570
 
11.6%
/ 4119
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 956864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 249973
26.1%
N 153993
16.1%
T 127046
13.3%
D 122137
12.8%
O 79009
 
8.3%
76186
 
8.0%
C 66400
 
6.9%
, 10570
 
1.1%
L 10011
 
1.0%
U 9806
 
1.0%
Other values (12) 51733
 
5.4%

Non-Motorist Substance Abuse
Categorical

High correlation  Imbalance  Missing 

Distinct14
Distinct (%)0.5%
Missing75950
Missing (%)96.1%
Memory size4.8 MiB
NONE DETECTED
2705 
UNKNOWN
 
169
ALCOHOL PRESENT
 
118
ALCOHOL CONTRIBUTED
 
34
N/A, NONE DETECTED
 
16
Other values (9)
 
15

Length

Max length36
Median length13
Mean length12.875695
Min length5

Characters and Unicode

Total characters39361
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowNONE DETECTED
2nd rowNONE DETECTED
3rd rowNONE DETECTED
4th rowNONE DETECTED
5th rowNONE DETECTED

Common Values

ValueCountFrequency (%)
NONE DETECTED 2705
 
3.4%
UNKNOWN 169
 
0.2%
ALCOHOL PRESENT 118
 
0.1%
ALCOHOL CONTRIBUTED 34
 
< 0.1%
N/A, NONE DETECTED 16
 
< 0.1%
MEDICATION PRESENT 3
 
< 0.1%
ILLEGAL DRUG PRESENT 3
 
< 0.1%
ILLEGAL DRUG CONTRIBUTED 2
 
< 0.1%
COMBINATION CONTRIBUTED 2
 
< 0.1%
ALCOHOL CONTRIBUTED, ALCOHOL PRESENT 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
(Missing) 75950
96.1%

Length

2025-02-12T01:36:20.463003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 2722
45.6%
detected 2722
45.6%
unknown 171
 
2.9%
alcohol 154
 
2.6%
present 126
 
2.1%
contributed 39
 
0.7%
n/a 17
 
0.3%
illegal 5
 
0.1%
drug 5
 
0.1%
medication 3
 
0.1%
Other values (4) 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 11190
28.4%
N 6148
15.6%
T 5655
14.4%
D 5492
14.0%
O 3249
 
8.3%
C 2922
 
7.4%
2912
 
7.4%
L 323
 
0.8%
U 216
 
0.5%
A 182
 
0.5%
Other values (12) 1072
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 36413
92.5%
Space Separator 2912
 
7.4%
Other Punctuation 36
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 11190
30.7%
N 6148
16.9%
T 5655
15.5%
D 5492
15.1%
O 3249
 
8.9%
C 2922
 
8.0%
L 323
 
0.9%
U 216
 
0.6%
A 182
 
0.5%
K 171
 
0.5%
Other values (9) 865
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 19
52.8%
/ 17
47.2%
Space Separator
ValueCountFrequency (%)
2912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36413
92.5%
Common 2948
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 11190
30.7%
N 6148
16.9%
T 5655
15.5%
D 5492
15.1%
O 3249
 
8.9%
C 2922
 
8.0%
L 323
 
0.9%
U 216
 
0.6%
A 182
 
0.5%
K 171
 
0.5%
Other values (9) 865
 
2.4%
Common
ValueCountFrequency (%)
2912
98.8%
, 19
 
0.6%
/ 17
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 11190
28.4%
N 6148
15.6%
T 5655
14.4%
D 5492
14.0%
O 3249
 
8.3%
C 2922
 
7.4%
2912
 
7.4%
L 323
 
0.8%
U 216
 
0.5%
A 182
 
0.5%
Other values (12) 1072
 
2.7%

First Harmful Event
Categorical

Imbalance 

Distinct25
Distinct (%)< 0.1%
Missing518
Missing (%)0.7%
Memory size5.2 MiB
OTHER VEHICLE
57829 
FIXED OBJECT
9055 
PARKED VEHICLE
 
4789
PEDESTRIAN
 
2674
OFF ROAD
 
949
Other values (20)
 
3193

Length

Max length30
Median length13
Mean length12.614213
Min length5

Characters and Unicode

Total characters990077
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER VEHICLE
2nd rowPARKED VEHICLE
3rd rowPARKED VEHICLE
4th rowOTHER VEHICLE
5th rowPARKED VEHICLE

Common Values

ValueCountFrequency (%)
OTHER VEHICLE 57829
73.2%
FIXED OBJECT 9055
 
11.5%
PARKED VEHICLE 4789
 
6.1%
PEDESTRIAN 2674
 
3.4%
OFF ROAD 949
 
1.2%
ANIMAL 876
 
1.1%
BICYCLE 770
 
1.0%
OTHER OBJECT 534
 
0.7%
OTHER 256
 
0.3%
OVERTURN 233
 
0.3%
Other values (15) 524
 
0.7%
(Missing) 518
 
0.7%

Length

2025-02-12T01:36:20.740895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vehicle 62654
41.1%
other 58854
38.6%
object 9633
 
6.3%
fixed 9055
 
5.9%
parked 4789
 
3.1%
pedestrian 2674
 
1.8%
off 949
 
0.6%
road 949
 
0.6%
animal 876
 
0.6%
bicycle 770
 
0.5%
Other values (27) 1117
 
0.7%

Most occurring characters

ValueCountFrequency (%)
E 214378
21.7%
H 121563
12.3%
I 76406
 
7.7%
C 74263
 
7.5%
73831
 
7.5%
T 71502
 
7.2%
O 71364
 
7.2%
R 67977
 
6.9%
L 64841
 
6.5%
V 62973
 
6.4%
Other values (16) 90979
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 916234
92.5%
Space Separator 73831
 
7.5%
Dash Punctuation 12
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 214378
23.4%
H 121563
13.3%
I 76406
 
8.3%
C 74263
 
8.1%
T 71502
 
7.8%
O 71364
 
7.8%
R 67977
 
7.4%
L 64841
 
7.1%
V 62973
 
6.9%
D 17586
 
1.9%
Other values (14) 73381
 
8.0%
Space Separator
ValueCountFrequency (%)
73831
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 916234
92.5%
Common 73843
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 214378
23.4%
H 121563
13.3%
I 76406
 
8.3%
C 74263
 
8.1%
T 71502
 
7.8%
O 71364
 
7.8%
R 67977
 
7.4%
L 64841
 
7.1%
V 62973
 
6.9%
D 17586
 
1.9%
Other values (14) 73381
 
8.0%
Common
ValueCountFrequency (%)
73831
> 99.9%
- 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 990077
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 214378
21.7%
H 121563
12.3%
I 76406
 
7.7%
C 74263
 
7.5%
73831
 
7.5%
T 71502
 
7.2%
O 71364
 
7.2%
R 67977
 
6.9%
L 64841
 
6.5%
V 62973
 
6.4%
Other values (16) 90979
9.2%

Second Harmful Event
Categorical

Imbalance  Missing 

Distinct24
Distinct (%)0.1%
Missing58373
Missing (%)73.9%
Memory size4.9 MiB
OTHER VEHICLE
10733 
FIXED OBJECT
6101 
PARKED VEHICLE
1411 
OFF ROAD
 
624
OVERTURN
 
559
Other values (19)
1206 

Length

Max length30
Median length13
Mean length12.36939
Min length5

Characters and Unicode

Total characters255230
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOTHER VEHICLE
2nd rowOTHER VEHICLE
3rd rowPARKED VEHICLE
4th rowFIXED OBJECT
5th rowEXPLOSION OR FIRE

Common Values

ValueCountFrequency (%)
OTHER VEHICLE 10733
 
13.6%
FIXED OBJECT 6101
 
7.7%
PARKED VEHICLE 1411
 
1.8%
OFF ROAD 624
 
0.8%
OVERTURN 559
 
0.7%
OTHER OBJECT 516
 
0.7%
PEDESTRIAN 239
 
0.3%
OTHER 124
 
0.2%
UNKNOWN 66
 
0.1%
BICYCLE 63
 
0.1%
Other values (14) 198
 
0.3%
(Missing) 58373
73.9%

Length

2025-02-12T01:36:21.065369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vehicle 12168
30.2%
other 11443
28.4%
object 6626
16.4%
fixed 6101
15.1%
parked 1411
 
3.5%
off 624
 
1.5%
road 624
 
1.5%
overturn 559
 
1.4%
pedestrian 239
 
0.6%
unknown 66
 
0.2%
Other values (25) 445
 
1.1%

Most occurring characters

ValueCountFrequency (%)
E 51219
20.1%
H 23630
9.3%
O 20242
 
7.9%
19672
 
7.7%
C 19053
 
7.5%
T 18910
 
7.4%
I 18746
 
7.3%
R 14976
 
5.9%
V 12768
 
5.0%
L 12448
 
4.9%
Other values (16) 43566
17.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 235552
92.3%
Space Separator 19672
 
7.7%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 51219
21.7%
H 23630
10.0%
O 20242
 
8.6%
C 19053
 
8.1%
T 18910
 
8.0%
I 18746
 
8.0%
R 14976
 
6.4%
V 12768
 
5.4%
L 12448
 
5.3%
D 8422
 
3.6%
Other values (14) 35138
14.9%
Space Separator
ValueCountFrequency (%)
19672
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 235552
92.3%
Common 19678
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 51219
21.7%
H 23630
10.0%
O 20242
 
8.6%
C 19053
 
8.1%
T 18910
 
8.0%
I 18746
 
8.0%
R 14976
 
6.4%
V 12768
 
5.4%
L 12448
 
5.3%
D 8422
 
3.6%
Other values (14) 35138
14.9%
Common
ValueCountFrequency (%)
19672
> 99.9%
- 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 51219
20.1%
H 23630
9.3%
O 20242
 
7.9%
19672
 
7.7%
C 19053
 
7.5%
T 18910
 
7.4%
I 18746
 
7.3%
R 14976
 
5.9%
V 12768
 
5.0%
L 12448
 
4.9%
Other values (16) 43566
17.1%

Junction
Categorical

Missing 

Distinct12
Distinct (%)< 0.1%
Missing12783
Missing (%)16.2%
Memory size5.3 MiB
INTERSECTION
29859 
NON INTERSECTION
22163 
INTERSECTION RELATED
10103 
COMMERCIAL DRIVEWAY
 
1085
INTERCHANGE RELATED
 
966
Other values (7)
 
2048

Length

Max length22
Median length20
Mean length14.76732
Min length5

Characters and Unicode

Total characters977951
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNON INTERSECTION
2nd rowNON INTERSECTION
3rd rowNON INTERSECTION
4th rowNON INTERSECTION
5th rowOTHER

Common Values

ValueCountFrequency (%)
INTERSECTION 29859
37.8%
NON INTERSECTION 22163
28.1%
INTERSECTION RELATED 10103
 
12.8%
COMMERCIAL DRIVEWAY 1085
 
1.4%
INTERCHANGE RELATED 966
 
1.2%
OTHER 863
 
1.1%
RESIDENTIAL DRIVEWAY 450
 
0.6%
CROSSOVER RELATED 362
 
0.5%
OTHER DRIVEWAY 250
 
0.3%
UNKNOWN 75
 
0.1%
Other values (2) 48
 
0.1%
(Missing) 12783
16.2%

Length

2025-02-12T01:36:21.413489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
intersection 62125
61.1%
non 22163
 
21.8%
related 11431
 
11.2%
driveway 1785
 
1.8%
other 1113
 
1.1%
commercial 1085
 
1.1%
interchange 966
 
1.0%
residential 450
 
0.4%
crossover 362
 
0.4%
unknown 75
 
0.1%
Other values (4) 80
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 171199
17.5%
E 154337
15.8%
T 138210
14.1%
I 129018
13.2%
O 87301
8.9%
R 79727
8.2%
C 65639
 
6.7%
S 63331
 
6.5%
35411
 
3.6%
A 15797
 
1.6%
Other values (10) 37981
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 942540
96.4%
Space Separator 35411
 
3.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 171199
18.2%
E 154337
16.4%
T 138210
14.7%
I 129018
13.7%
O 87301
9.3%
R 79727
8.5%
C 65639
 
7.0%
S 63331
 
6.7%
A 15797
 
1.7%
D 13682
 
1.5%
Other values (9) 24299
 
2.6%
Space Separator
ValueCountFrequency (%)
35411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 942540
96.4%
Common 35411
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 171199
18.2%
E 154337
16.4%
T 138210
14.7%
I 129018
13.7%
O 87301
9.3%
R 79727
8.5%
C 65639
 
7.0%
S 63331
 
6.7%
A 15797
 
1.7%
D 13682
 
1.5%
Other values (9) 24299
 
2.6%
Common
ValueCountFrequency (%)
35411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 977951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 171199
17.5%
E 154337
15.8%
T 138210
14.1%
I 129018
13.2%
O 87301
8.9%
R 79727
8.2%
C 65639
 
6.7%
S 63331
 
6.5%
35411
 
3.6%
A 15797
 
1.6%
Other values (10) 37981
 
3.9%

Intersection Type
Categorical

Imbalance  Missing 

Distinct8
Distinct (%)< 0.1%
Missing34145
Missing (%)43.2%
Memory size5.3 MiB
FOUR-WAY INTERSECTION
28337 
T-INTERSECTION
12860 
OTHER
 
2404
Y-INTERSECTION
 
583
ROUNDABOUT
 
279
Other values (3)
 
399

Length

Max length21
Median length21
Mean length17.917012
Min length5

Characters and Unicode

Total characters803793
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowFOUR-WAY INTERSECTION
3rd rowUNKNOWN
4th rowT-INTERSECTION
5th rowT-INTERSECTION

Common Values

ValueCountFrequency (%)
FOUR-WAY INTERSECTION 28337
35.9%
T-INTERSECTION 12860
 
16.3%
OTHER 2404
 
3.0%
Y-INTERSECTION 583
 
0.7%
ROUNDABOUT 279
 
0.4%
FIVE-POINT OR MORE 194
 
0.2%
TRAFFIC CIRCLE 111
 
0.1%
UNKNOWN 94
 
0.1%
(Missing) 34145
43.2%

Length

2025-02-12T01:36:21.621770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:21.856638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
four-way 28337
38.5%
intersection 28337
38.5%
t-intersection 12860
17.4%
other 2404
 
3.3%
y-intersection 583
 
0.8%
roundabout 279
 
0.4%
five-point 194
 
0.3%
or 194
 
0.3%
more 194
 
0.3%
traffic 111
 
0.2%
Other values (2) 205
 
0.3%

Most occurring characters

ValueCountFrequency (%)
T 99408
12.4%
E 86463
10.8%
N 84315
10.5%
I 84170
10.5%
O 73755
9.2%
R 73410
9.1%
C 42113
 
5.2%
- 41974
 
5.2%
S 41780
 
5.2%
U 28989
 
3.6%
Other values (13) 147416
18.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 732983
91.2%
Dash Punctuation 41974
 
5.2%
Space Separator 28836
 
3.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 99408
13.6%
E 86463
11.8%
N 84315
11.5%
I 84170
11.5%
O 73755
10.1%
R 73410
10.0%
C 42113
5.7%
S 41780
5.7%
U 28989
 
4.0%
Y 28920
 
3.9%
Other values (11) 89660
12.2%
Dash Punctuation
ValueCountFrequency (%)
- 41974
100.0%
Space Separator
ValueCountFrequency (%)
28836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 732983
91.2%
Common 70810
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 99408
13.6%
E 86463
11.8%
N 84315
11.5%
I 84170
11.5%
O 73755
10.1%
R 73410
10.0%
C 42113
5.7%
S 41780
5.7%
U 28989
 
4.0%
Y 28920
 
3.9%
Other values (11) 89660
12.2%
Common
ValueCountFrequency (%)
- 41974
59.3%
28836
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 803793
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 99408
12.4%
E 86463
10.8%
N 84315
10.5%
I 84170
10.5%
O 73755
9.2%
R 73410
9.1%
C 42113
 
5.2%
- 41974
 
5.2%
S 41780
 
5.2%
U 28989
 
3.6%
Other values (13) 147416
18.3%

Road Alignment
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing399
Missing (%)0.5%
Memory size4.9 MiB
STRAIGHT
68713 
CURVE RIGHT
 
4813
CURVE LEFT
 
4467
OTHER
 
494
UNKNOWN
 
121

Length

Max length11
Median length8
Mean length8.2769438
Min length5

Characters and Unicode

Total characters650634
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTRAIGHT
2nd rowSTRAIGHT
3rd rowSTRAIGHT
4th rowCURVE LEFT
5th rowSTRAIGHT

Common Values

ValueCountFrequency (%)
STRAIGHT 68713
87.0%
CURVE RIGHT 4813
 
6.1%
CURVE LEFT 4467
 
5.7%
OTHER 494
 
0.6%
UNKNOWN 121
 
0.2%
(Missing) 399
 
0.5%

Length

2025-02-12T01:36:22.182751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:22.376058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
straight 68713
78.2%
curve 9280
 
10.6%
right 4813
 
5.5%
left 4467
 
5.1%
other 494
 
0.6%
unknown 121
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 147200
22.6%
R 83300
12.8%
H 74020
11.4%
I 73526
11.3%
G 73526
11.3%
S 68713
10.6%
A 68713
10.6%
E 14241
 
2.2%
U 9401
 
1.4%
9280
 
1.4%
Other values (8) 28714
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 641354
98.6%
Space Separator 9280
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 147200
23.0%
R 83300
13.0%
H 74020
11.5%
I 73526
11.5%
G 73526
11.5%
S 68713
10.7%
A 68713
10.7%
E 14241
 
2.2%
U 9401
 
1.5%
V 9280
 
1.4%
Other values (7) 19434
 
3.0%
Space Separator
ValueCountFrequency (%)
9280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 641354
98.6%
Common 9280
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 147200
23.0%
R 83300
13.0%
H 74020
11.5%
I 73526
11.5%
G 73526
11.5%
S 68713
10.7%
A 68713
10.7%
E 14241
 
2.2%
U 9401
 
1.5%
V 9280
 
1.4%
Other values (7) 19434
 
3.0%
Common
ValueCountFrequency (%)
9280
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 650634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 147200
22.6%
R 83300
12.8%
H 74020
11.4%
I 73526
11.3%
G 73526
11.3%
S 68713
10.6%
A 68713
10.6%
E 14241
 
2.2%
U 9401
 
1.4%
9280
 
1.4%
Other values (8) 28714
 
4.4%

Road Condition
Categorical

Imbalance  Missing 

Distinct10
Distinct (%)< 0.1%
Missing3138
Missing (%)4.0%
Memory size5.0 MiB
NO DEFECTS
74673 
OTHER
 
312
HOLES RUTS ETC
 
270
UNKNOWN
 
189
LOOSE SURFACE MATERIAL
 
147
Other values (5)
 
278

Length

Max length24
Median length10
Mean length10.032187
Min length5

Characters and Unicode

Total characters761132
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO DEFECTS
2nd rowNO DEFECTS
3rd rowNO DEFECTS
4th rowNO DEFECTS
5th rowNO DEFECTS

Common Values

ValueCountFrequency (%)
NO DEFECTS 74673
94.5%
OTHER 312
 
0.4%
HOLES RUTS ETC 270
 
0.3%
UNKNOWN 189
 
0.2%
LOOSE SURFACE MATERIAL 147
 
0.2%
FOREIGN MATERIAL 118
 
0.1%
VIEW OBSTRUCTED 67
 
0.1%
SHOULDER DEFECT 67
 
0.1%
OBSTRUCTION NOT LIGHTED 17
 
< 0.1%
OBSTRUCTION NOT SIGNALED 9
 
< 0.1%
(Missing) 3138
 
4.0%

Length

2025-02-12T01:36:22.674508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:22.894395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 74673
49.2%
defects 74673
49.2%
other 312
 
0.2%
holes 270
 
0.2%
ruts 270
 
0.2%
etc 270
 
0.2%
material 265
 
0.2%
unknown 189
 
0.1%
loose 147
 
0.1%
surface 147
 
0.1%
Other values (9) 464
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E 151236
19.9%
T 76086
10.0%
O 76068
10.0%
75811
10.0%
S 75676
9.9%
N 75419
9.9%
C 75250
9.9%
F 75005
9.9%
D 74900
9.8%
R 1272
 
0.2%
Other values (11) 4409
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 685321
90.0%
Space Separator 75811
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 151236
22.1%
T 76086
11.1%
O 76068
11.1%
S 75676
11.0%
N 75419
11.0%
C 75250
11.0%
F 75005
10.9%
D 74900
10.9%
R 1272
 
0.2%
L 775
 
0.1%
Other values (10) 3634
 
0.5%
Space Separator
ValueCountFrequency (%)
75811
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 685321
90.0%
Common 75811
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 151236
22.1%
T 76086
11.1%
O 76068
11.1%
S 75676
11.0%
N 75419
11.0%
C 75250
11.0%
F 75005
10.9%
D 74900
10.9%
R 1272
 
0.2%
L 775
 
0.1%
Other values (10) 3634
 
0.5%
Common
ValueCountFrequency (%)
75811
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 761132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 151236
19.9%
T 76086
10.0%
O 76068
10.0%
75811
10.0%
S 75676
9.9%
N 75419
9.9%
C 75250
9.9%
F 75005
9.9%
D 74900
9.8%
R 1272
 
0.2%
Other values (11) 4409
 
0.6%

Road Division
Categorical

Missing 

Distinct7
Distinct (%)< 0.1%
Missing1141
Missing (%)1.4%
Memory size6.8 MiB
TWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER
36979 
TWO-WAY, NOT DIVIDED
28424 
TWO-WAY, DIVIDED, UNPROTECTED PAINTED MIN 4 FEET
9123 
ONE-WAY TRAFFICWAY
 
1845
OTHER
 
1077
Other values (2)
 
418

Length

Max length48
Median length41
Mean length33.106902
Min length5

Characters and Unicode

Total characters2577902
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTWO-WAY, NOT DIVIDED
2nd rowTWO-WAY, NOT DIVIDED
3rd rowTWO-WAY, NOT DIVIDED
4th rowTWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER
5th rowTWO-WAY, NOT DIVIDED

Common Values

ValueCountFrequency (%)
TWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER 36979
46.8%
TWO-WAY, NOT DIVIDED 28424
36.0%
TWO-WAY, DIVIDED, UNPROTECTED PAINTED MIN 4 FEET 9123
 
11.5%
ONE-WAY TRAFFICWAY 1845
 
2.3%
OTHER 1077
 
1.4%
TWO-WAY, NOT DIVIDED WITH A CONTINUOUS LEFT TURN 338
 
0.4%
UNKNOWN 80
 
0.1%
(Missing) 1141
 
1.4%

Length

2025-02-12T01:36:23.194075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-12T01:36:23.419765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
two-way 74864
21.9%
divided 74864
21.9%
positive 36979
10.8%
median 36979
10.8%
barrier 36979
10.8%
not 28762
 
8.4%
min 9123
 
2.7%
4 9123
 
2.7%
feet 9123
 
2.7%
painted 9123
 
2.7%
Other values (10) 15660
 
4.6%

Most occurring characters

ValueCountFrequency (%)
I 318411
12.4%
D 279817
10.9%
263713
10.2%
E 234676
9.1%
T 181371
 
7.0%
A 163818
 
6.4%
W 153836
 
6.0%
O 153406
 
6.0%
R 123320
 
4.8%
, 120966
 
4.7%
Other values (15) 584568
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2107391
81.7%
Space Separator 263713
 
10.2%
Other Punctuation 120966
 
4.7%
Dash Punctuation 76709
 
3.0%
Decimal Number 9123
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 318411
15.1%
D 279817
13.3%
E 234676
11.1%
T 181371
8.6%
A 163818
7.8%
W 153836
7.3%
O 153406
7.3%
R 123320
 
5.9%
V 111843
 
5.3%
N 96209
 
4.6%
Other values (11) 290684
13.8%
Space Separator
ValueCountFrequency (%)
263713
100.0%
Other Punctuation
ValueCountFrequency (%)
, 120966
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76709
100.0%
Decimal Number
ValueCountFrequency (%)
4 9123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2107391
81.7%
Common 470511
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 318411
15.1%
D 279817
13.3%
E 234676
11.1%
T 181371
8.6%
A 163818
7.8%
W 153836
7.3%
O 153406
7.3%
R 123320
 
5.9%
V 111843
 
5.3%
N 96209
 
4.6%
Other values (11) 290684
13.8%
Common
ValueCountFrequency (%)
263713
56.0%
, 120966
25.7%
- 76709
 
16.3%
4 9123
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2577902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 318411
12.4%
D 279817
10.9%
263713
10.2%
E 234676
9.1%
T 181371
 
7.0%
A 163818
 
6.4%
W 153836
 
6.0%
O 153406
 
6.0%
R 123320
 
4.8%
, 120966
 
4.7%
Other values (15) 584568
22.7%

Latitude
Real number (ℝ)

High correlation 

Distinct70456
Distinct (%)89.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean39.084363
Minimum37.72
Maximum39.988369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size617.4 KiB
2025-02-12T01:36:23.682375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum37.72
5-th percentile38.986131
Q139.024805
median39.075789
Q339.140567
95-th percentile39.204738
Maximum39.988369
Range2.268369
Interquartile range (IQR)0.11576165

Descriptive statistics

Standard deviation0.072991609
Coefficient of variation (CV)0.0018675399
Kurtosis3.7816321
Mean39.084363
Median Absolute Deviation (MAD)0.055378605
Skewness0.51918714
Sum3087899.2
Variance0.005327775
MonotonicityNot monotonic
2025-02-12T01:36:23.962516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.953 20
 
< 0.1%
39.11342767 13
 
< 0.1%
39.04627667 13
 
< 0.1%
39.045425 12
 
< 0.1%
39.07997592 10
 
< 0.1%
39.11061 10
 
< 0.1%
39.07676629 7
 
< 0.1%
39.092455 7
 
< 0.1%
39.109775 7
 
< 0.1%
39.08277 7
 
< 0.1%
Other values (70446) 78900
99.9%
ValueCountFrequency (%)
37.72 1
 
< 0.1%
38.00812 1
 
< 0.1%
38.353495 1
 
< 0.1%
38.66493 1
 
< 0.1%
38.67069811 1
 
< 0.1%
38.743373 3
< 0.1%
38.78370267 1
 
< 0.1%
38.81807478 1
 
< 0.1%
38.84586333 1
 
< 0.1%
38.89012296 1
 
< 0.1%
ValueCountFrequency (%)
39.988369 1
 
< 0.1%
39.987474 1
 
< 0.1%
39.972695 1
 
< 0.1%
39.96483333 1
 
< 0.1%
39.83806818 1
 
< 0.1%
39.72 5
< 0.1%
39.67337039 1
 
< 0.1%
39.63653167 1
 
< 0.1%
39.604 1
 
< 0.1%
39.55178002 1
 
< 0.1%

Longitude
Real number (ℝ)

High correlation 

Distinct71920
Distinct (%)91.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-77.11381
Minimum-79.486
Maximum-75.527708
Zeros0
Zeros (%)0.0%
Negative79006
Negative (%)> 99.9%
Memory size617.4 KiB
2025-02-12T01:36:24.309419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-79.486
5-th percentile-77.267894
Q1-77.190174
median-77.106244
Q3-77.040298
95-th percentile-76.97474
Maximum-75.527708
Range3.9582921
Interquartile range (IQR)0.14987584

Descriptive statistics

Standard deviation0.098534694
Coefficient of variation (CV)-0.0012777827
Kurtosis26.078488
Mean-77.11381
Median Absolute Deviation (MAD)0.07564819
Skewness-1.2117052
Sum-6092453.7
Variance0.009709086
MonotonicityNot monotonic
2025-02-12T01:36:24.640159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-77.338 20
 
< 0.1%
-76.990695 14
 
< 0.1%
-77.23648183 13
 
< 0.1%
-76.99073667 12
 
< 0.1%
-76.98979833 10
 
< 0.1%
-77.13826298 10
 
< 0.1%
-77.04575667 8
 
< 0.1%
-77.11714305 7
 
< 0.1%
-77.08949028 7
 
< 0.1%
-76.91044 7
 
< 0.1%
Other values (71910) 78898
99.9%
ValueCountFrequency (%)
-79.486 5
< 0.1%
-79.48 1
 
< 0.1%
-77.75 1
 
< 0.1%
-77.65175333 1
 
< 0.1%
-77.54699707 3
< 0.1%
-77.53852509 1
 
< 0.1%
-77.517055 1
 
< 0.1%
-77.51681167 1
 
< 0.1%
-77.51665667 1
 
< 0.1%
-77.51308333 1
 
< 0.1%
ValueCountFrequency (%)
-75.52770787 1
< 0.1%
-75.97595215 1
< 0.1%
-76.32256482 1
< 0.1%
-76.4702388 1
< 0.1%
-76.47317333 1
< 0.1%
-76.519245 1
< 0.1%
-76.56337646 1
< 0.1%
-76.63154167 1
< 0.1%
-76.65710449 1
< 0.1%
-76.664 1
< 0.1%
Distinct78425
Distinct (%)99.3%
Missing1
Missing (%)< 0.1%
Memory size6.2 MiB
2025-02-12T01:36:25.083517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length27
Mean length25.713174
Min length13

Characters and Unicode

Total characters2031495
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78042 ?
Unique (%)98.8%

Sample

1st row(39.11311333, -77.05759167)
2nd row(38.98244333, -77.079235)
3rd row(39.189845, -77.230325)
4th row(39.04169833, -77.050125)
5th row(39.08472, -77.1482)
ValueCountFrequency (%)
38.953 20
 
< 0.1%
77.338 20
 
< 0.1%
76.990695 14
 
< 0.1%
39.04627667 13
 
< 0.1%
39.11342767 13
 
< 0.1%
77.23648183 13
 
< 0.1%
39.045425 12
 
< 0.1%
76.99073667 12
 
< 0.1%
39.11061 10
 
< 0.1%
76.98979833 10
 
< 0.1%
Other values (142366) 157875
99.9%
2025-02-12T01:36:25.649235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 265163
13.1%
3 241693
11.9%
9 168985
 
8.3%
. 158012
 
7.8%
1 145606
 
7.2%
0 137172
 
6.8%
6 136533
 
6.7%
8 107797
 
5.3%
5 99495
 
4.9%
2 97163
 
4.8%
Other values (6) 473876
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1478453
72.8%
Other Punctuation 237018
 
11.7%
Open Punctuation 79006
 
3.9%
Space Separator 79006
 
3.9%
Dash Punctuation 79006
 
3.9%
Close Punctuation 79006
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 265163
17.9%
3 241693
16.3%
9 168985
11.4%
1 145606
9.8%
0 137172
9.3%
6 136533
9.2%
8 107797
7.3%
5 99495
 
6.7%
2 97163
 
6.6%
4 78846
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 158012
66.7%
, 79006
33.3%
Open Punctuation
ValueCountFrequency (%)
( 79006
100.0%
Space Separator
ValueCountFrequency (%)
79006
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79006
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2031495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 265163
13.1%
3 241693
11.9%
9 168985
 
8.3%
. 158012
 
7.8%
1 145606
 
7.2%
0 137172
 
6.8%
6 136533
 
6.7%
8 107797
 
5.3%
5 99495
 
4.9%
2 97163
 
4.8%
Other values (6) 473876
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2031495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 265163
13.1%
3 241693
11.9%
9 168985
 
8.3%
. 158012
 
7.8%
1 145606
 
7.2%
0 137172
 
6.8%
6 136533
 
6.7%
8 107797
 
5.3%
5 99495
 
4.9%
2 97163
 
4.8%
Other values (6) 473876
23.3%

Interactions

2025-02-12T01:36:05.789429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:03.733666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:04.430341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:05.136185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:05.966754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:03.906484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:04.596466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:05.290087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:06.152826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:04.067235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:04.758244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:05.455000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:06.338582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:04.227302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:04.926599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-12T01:36:05.597089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-12T01:36:25.786504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACRS Report TypeAgency NameAt FaultCollision TypeDirectionDistanceDistance UnitDriver Substance AbuseFirst Harmful EventHit/RunIntersection TypeJunctionLane DirectionLane TypeLatitudeLightLongitudeMunicipalityNon-Motorist Substance AbuseNumber of LanesRelated Non-MotoristRoad AlignmentRoad ConditionRoad DivisionRoad GradeRoute TypeSecond Harmful EventSurface ConditionTraffic ControlWeather
ACRS Report Type1.0000.0350.1250.1540.0260.0230.0170.1300.2190.1840.0420.0780.0210.0660.0270.0480.0240.0490.2400.0100.1850.0290.0190.0520.0240.0500.1500.0290.0730.029
Agency Name0.0351.0000.0410.0370.0460.0100.0710.0390.0310.0600.0360.0350.0340.0350.0930.0270.1510.4260.0800.0000.1000.0330.0110.0450.0330.2360.0440.0170.0280.023
At Fault0.1250.0411.0000.1420.0000.0070.0300.0520.3880.0390.0400.0470.0140.0990.0100.0500.0020.0510.2000.0110.0460.0290.0440.0270.0260.0240.2120.0290.0430.026
Collision Type0.1540.0370.1421.0000.0800.0600.1360.1060.2530.2490.1250.1820.0820.2310.0580.1200.0530.0600.0000.0480.1450.1720.0600.1340.0730.0810.1900.0730.1680.065
Direction0.0260.0460.0000.0801.0000.0570.0540.0340.0570.0230.0420.1480.3590.0750.0210.0240.0260.1390.0000.0000.0000.0390.0160.0740.0290.1720.0330.0150.0990.011
Distance0.0230.0100.0070.0600.0571.0000.0700.0240.0510.0150.0520.1320.0070.0780.0030.0230.0090.0000.000-0.1070.0000.0420.0120.0340.0290.0200.0300.0160.0950.015
Distance Unit0.0170.0710.0300.1360.0540.0701.0000.0450.1340.0380.1370.2140.0820.1910.0650.0910.0440.0830.0000.0330.0500.1220.0730.0790.0740.1140.0880.0630.1670.037
Driver Substance Abuse0.1300.0390.0520.1060.0340.0240.0451.0000.1020.7290.0490.0570.0390.1170.0290.1500.0440.0370.0000.0000.0460.0840.0450.0690.0480.0610.0870.0630.0680.065
First Harmful Event0.2190.0310.3880.2530.0570.0510.1340.1021.0000.1460.1450.1950.0630.2370.0480.1220.0390.0400.0000.0720.4040.1870.0800.1530.0900.0980.3280.0830.2000.055
Hit/Run0.1840.0600.0390.2490.0230.0150.0380.7290.1461.0000.0440.0890.0410.1450.0660.1510.0750.1180.0000.0000.0000.0450.0580.0580.0600.0430.1780.1200.0820.115
Intersection Type0.0420.0360.0400.1250.0420.0520.1370.0490.1450.0441.0000.3230.1340.2620.0300.1140.0240.0990.0000.0720.0380.3290.1830.2890.2030.1110.1500.1270.2680.063
Junction0.0780.0350.0470.1820.1480.1320.2140.0570.1950.0890.3231.0000.0980.3470.0230.0920.0290.0510.0000.0810.0570.2530.1210.2590.1630.2120.1330.0810.3720.045
Lane Direction0.0210.0340.0140.0820.3590.0070.0820.0390.0630.0410.1340.0981.0000.1310.0130.0630.0110.1050.0000.0270.0000.1090.0860.1350.0950.1140.0730.0600.0760.047
Lane Type0.0660.0350.0990.2310.0750.0780.1910.1170.2370.1450.2620.3470.1311.0000.0380.1140.0480.0560.0000.0330.0520.2760.0930.2860.1290.3030.1910.0820.2270.056
Latitude0.0270.0930.0100.0580.0210.0030.0650.0290.0480.0660.0300.0230.0130.0381.0000.029-0.6280.3470.000-0.1280.0380.0420.0070.0380.0220.0660.0780.0110.0170.013
Light0.0480.0270.0500.1200.0240.0230.0910.1500.1220.1510.1140.0920.0630.1140.0291.0000.0240.0420.1040.0400.1060.1110.0910.1030.0790.0320.1110.1670.1100.176
Longitude0.0240.1510.0020.0530.0260.0090.0440.0440.0390.0750.0240.0290.0110.048-0.6280.0241.0000.4900.0180.0900.0690.0350.0120.0190.0160.1260.0680.0330.0200.032
Municipality0.0490.4260.0510.0600.1390.0000.0830.0370.0400.1180.0990.0510.1050.0560.3470.0420.4901.0000.0001.0000.0000.0860.0270.1040.0610.1400.0000.0540.0840.018
Non-Motorist Substance Abuse0.2400.0800.2000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1040.0180.0001.0001.0000.2600.0290.0000.0290.0000.0110.0000.0000.0510.000
Number of Lanes0.0100.0000.0110.0480.000-0.1070.0330.0000.0720.0000.0720.0810.0270.033-0.1280.0400.0901.0001.0001.0001.0000.0640.0500.0790.0590.0280.0820.0350.0560.039
Related Non-Motorist0.1850.1000.0460.1450.0000.0000.0500.0460.4040.0000.0380.0570.0000.0520.0380.1060.0690.0000.2601.0001.0000.0330.0310.0390.0560.0360.3410.0660.1030.096
Road Alignment0.0290.0330.0290.1720.0390.0420.1220.0840.1870.0450.3290.2530.1090.2760.0420.1110.0350.0860.0290.0640.0331.0000.2380.3140.3920.1020.2040.1360.2180.093
Road Condition0.0190.0110.0440.0600.0160.0120.0730.0450.0800.0580.1830.1210.0860.0930.0070.0910.0120.0270.0000.0500.0310.2381.0000.2110.1880.0180.0950.2040.1220.087
Road Division0.0520.0450.0270.1340.0740.0340.0790.0690.1530.0580.2890.2590.1350.2860.0380.1030.0190.1040.0290.0790.0390.3140.2111.0000.2430.1860.1730.1130.2240.078
Road Grade0.0240.0330.0260.0730.0290.0290.0740.0480.0900.0600.2030.1630.0950.1290.0220.0790.0160.0610.0000.0590.0560.3920.1880.2431.0000.0340.1360.1100.1380.074
Route Type0.0500.2360.0240.0810.1720.0200.1140.0610.0980.0430.1110.2120.1140.3030.0660.0320.1260.1400.0110.0280.0360.1020.0180.1860.0341.0000.1000.0330.0840.027
Second Harmful Event0.1500.0440.2120.1900.0330.0300.0880.0870.3280.1780.1500.1330.0730.1910.0780.1110.0680.0000.0000.0820.3410.2040.0950.1730.1360.1001.0000.0660.1350.053
Surface Condition0.0290.0170.0290.0730.0150.0160.0630.0630.0830.1200.1270.0810.0600.0820.0110.1670.0330.0540.0000.0350.0660.1360.2040.1130.1100.0330.0661.0000.0750.415
Traffic Control0.0730.0280.0430.1680.0990.0950.1670.0680.2000.0820.2680.3720.0760.2270.0170.1100.0200.0840.0510.0560.1030.2180.1220.2240.1380.0840.1350.0751.0000.046
Weather0.0290.0230.0260.0650.0110.0150.0370.0650.0550.1150.0630.0450.0470.0560.0130.1760.0320.0180.0000.0390.0960.0930.0870.0780.0740.0270.0530.4150.0461.000

Missing values

2025-02-12T01:36:07.039188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-12T01:36:08.108395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-12T01:36:10.614100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Report NumberLocal Case NumberAgency NameACRS Report TypeCrash Date/TimeHit/RunRoute TypeLane DirectionLane TypeNumber of LanesDirectionDistanceDistance UnitRoad GradeRoad NameCross-Street NameOff-Road DescriptionMunicipalityRelated Non-MotoristAt FaultCollision TypeWeatherSurface ConditionLightTraffic ControlDriver Substance AbuseNon-Motorist Substance AbuseFirst Harmful EventSecond Harmful EventJunctionIntersection TypeRoad AlignmentRoad ConditionRoad DivisionLatitudeLongitudeLocation
0MCP1123002M190010046Montgomery County PoliceInjury Crash03/04/2019 08:41:00 AMNoMaryland (State)WestNaN2.0East200.0FEETGRADE DOWNHILLNORBECK RDWINTERGATE DRNaNNaNNaNDRIVERSAME DIR REAR ENDCLOUDYDRYDAYLIGHTNaNNONE DETECTEDNaNOTHER VEHICLENaNNON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.113113-77.057592(39.11311333, -77.05759167)
1MCP2161000916028039Montgomery County PoliceProperty Damage Crash06/04/2016 07:14:00 PMYesCountyEastNaN1.0East500.0FEETLEVELTHORNAPPLE STLENHART DRNaNNaNNaNDRIVEROTHERCLEARDRYDAYLIGHTNaNUNKNOWNNaNPARKED VEHICLEOTHER VEHICLENON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED38.982443-77.079235(38.98244333, -77.079235)
2MCP2790000P15041420MONTGOMERYProperty Damage Crash08/18/2015 11:00:00 PMNoCountySouthNaN2.0South30.0FEETLEVELVALLEY BEND DRCROSS LAUREL DRNaNNaNNaNUNKNOWNOPPOSITE DIRECTION SIDESWIPECLEARDRYDARK LIGHTS ONNO CONTROLSNONE DETECTEDNaNPARKED VEHICLENaNNON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.189845-77.230325(39.189845, -77.230325)
3MCP3378000J230051006Montgomery County PoliceInjury Crash08/24/2023 07:46:00 AMNoMaryland (State)WestNaN4.0West50.0FEETLEVELUNIVERSITY BLVD WELKIN STNaNNaNNaNDRIVERSINGLE VEHICLECLOUDYDRYDAYLIGHTNO CONTROLSNONE DETECTEDNaNNaNNaNNON INTERSECTIONNaNCURVE LEFTNO DEFECTSTWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER39.041698-77.050125(39.04169833, -77.050125)
4DD5659000H230049130Rockville Police DepartmeProperty Damage Crash08/12/2023 04:28:00 PMYesNaNSouthNaN3.0South40.0FEETLEVELROCKVILLE PIKEPARK RDNaNNaNNaNDRIVERSAME DIRECTION SIDESWIPENaNDRYNaNNO CONTROLSNaNNaNOTHER VEHICLEOTHER VEHICLENaNNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.084720-77.148200(39.08472, -77.1482)
5MCP33190021230049349Montgomery County PoliceProperty Damage Crash08/14/2023 08:50:00 AMNoCountyUnknownOTHER0.0East400.0FEETNaNFENTON STBONIFANT STNaNNaNNaNDRIVERSINGLE VEHICLENaNNaNDAYLIGHTNaNNaNNaNNaNNaNOTHERNaNNaNNaNNaN38.994612-77.023368(38.99461167, -77.02336833)
6MCP3008003Z230059393Montgomery County PoliceProperty Damage Crash10/09/2023 03:40:00 AMNoCountyEastNaN2.0East50.0FEETLEVELDILSTON RDMOFFET RDNaNNaNNaNDRIVERHEAD ONCLEARDRYDARK LIGHTS ONNO CONTROLSNaNNaNPARKED VEHICLEPARKED VEHICLENON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.018215-76.983405(39.018215, -76.983405)
7MCP289200FC230059753Montgomery County PoliceProperty Damage Crash10/10/2023 08:59:00 PMNoCountySouthNaN1.0North50.0FEETLEVELBRUNETT AVELYCOMING STNaNNaNNaNDRIVERHEAD ONCLEARDRYDARK LIGHTS ONNO CONTROLSNONE DETECTEDNaNPARKED VEHICLENaNNaNNaNSTRAIGHTNaNTWO-WAY, NOT DIVIDED39.011715-77.021583(39.011715, -77.02158333)
8MCP2821002X230034109Montgomery County PoliceProperty Damage Crash07/17/2023 11:37:00 PMYesCountySouthNaN2.0South0.5MILELEVELFOUNDERS MILL DRFOUNDERS MILL CTNaNNaNNaNDRIVERSINGLE VEHICLECLEARDRYDARK -- UNKNOWN LIGHTINGNO CONTROLSALCOHOL PRESENTNaNFIXED OBJECTFIXED OBJECTNaNNaNCURVE LEFTNO DEFECTSTWO-WAY, NOT DIVIDED39.139848-77.150410(39.1398478, -77.15041002)
9MCP2771002W230061610Montgomery County PoliceProperty Damage Crash10/20/2023 12:41:00 PMYesCountyEastNaN2.0East10.0FEETNaNFREDALE STFARTHING DRNaNNaNNaNDRIVEROTHERCLOUDYDRYDAYLIGHTNaNNaNNaNPARKED VEHICLENaNNaNNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.061433-77.069168(39.06143272, -77.06916793)
Report NumberLocal Case NumberAgency NameACRS Report TypeCrash Date/TimeHit/RunRoute TypeLane DirectionLane TypeNumber of LanesDirectionDistanceDistance UnitRoad GradeRoad NameCross-Street NameOff-Road DescriptionMunicipalityRelated Non-MotoristAt FaultCollision TypeWeatherSurface ConditionLightTraffic ControlDriver Substance AbuseNon-Motorist Substance AbuseFirst Harmful EventSecond Harmful EventJunctionIntersection TypeRoad AlignmentRoad ConditionRoad DivisionLatitudeLongitudeLocation
78997MCP24870010180062868Montgomery County PoliceProperty Damage Crash12/17/2018 06:40:00 AMNoMaryland (State)WestNaN1.0West400.0FEETGRADE DOWNHILLDARNESTOWN RDBELLINGHAM DRNaNNaNNaNDRIVERSINGLE VEHICLECLEARDRYDARK NO LIGHTSNO CONTROLSNONE DETECTEDNaNANIMALNaNNON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.122642-77.323415(39.12264167, -77.323415)
78998MCP2116004C16027325Montgomery County PoliceInjury Crash06/01/2016 10:26:00 AMNoMaryland (State)NorthNaN4.0South200.0FEETHILL UPHILLFREDERICK RDE GUDE DRNaNNaNNaNDRIVERSAME DIR REAR ENDCLEARDRYDAYLIGHTTRAFFIC SIGNALNONE DETECTEDNaNOTHER VEHICLENaNINTERSECTION RELATEDFOUR-WAY INTERSECTIONSTRAIGHTNO DEFECTSTWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER39.105757-77.157678(39.10575667, -77.15767833)
78999MCP2904006Y190025093Montgomery County PoliceInjury Crash05/26/2019 06:00:00 PMNoCountyEastNaN1.0West0.0FEETGRADE DOWNHILLLANGLEY DRLINTON STNaNNaNBICYCLISTNONMOTORISTSTRAIGHT MOVEMENT ANGLECLEARDRYDAYLIGHTSTOP SIGNNONE DETECTEDNONE DETECTEDBICYCLENaNINTERSECTIONT-INTERSECTIONSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.002458-76.990948(39.00245833, -76.99094833)
79000MCP2656001F16052679Montgomery County PoliceProperty Damage Crash10/13/2016 04:29:00 PMYesUS (State)SouthNaN3.0North0.0FEETLEVELCOLUMBIA PIKEGREENCASTLE RDNaNNaNNaNDRIVERSAME DIR REAR ENDCLEARDRYNaNNO CONTROLSNONE DETECTEDNaNOTHER VEHICLENaNINTERSECTIONFOUR-WAY INTERSECTIONSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.087635-76.938610(39.087635, -76.93861)
79001MCP2853002116040600Montgomery County PoliceInjury Crash08/10/2016 07:41:00 PMNoCountySouthNaN1.0North0.0UNKNOWNLEVELTENBROOK DRFOREST GLEN RDNaNNaNNaNDRIVEROTHERCLEARDRYDAYLIGHTNO CONTROLSNaNNaNOTHER VEHICLEOTHER VEHICLENaNNaNSTRAIGHTNO DEFECTSTWO-WAY, DIVIDED, UNPROTECTED PAINTED MIN 4 FEET39.017652-77.031448(39.01765167, -77.03144833)
79002MCP003600CZ200005848Montgomery County PoliceProperty Damage Crash02/04/2020 05:50:00 PMYesCountyNorthNaN3.0South40.0FEETLEVELFATHER HURLEY BLVDCRYSTAL ROCK DRNaNNaNNaNDRIVERSAME DIR REAR ENDCLOUDYDRYDARK LIGHTS ONTRAFFIC SIGNALALCOHOL PRESENT, NONE DETECTEDNaNOTHER VEHICLENaNNON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, DIVIDED, UNPROTECTED PAINTED MIN 4 FEET39.193124-77.270317(39.19312391, -77.27031747)
79003DD5583001B16013581Rockville Police DepartmeProperty Damage Crash03/19/2016 03:55:00 PMNoCountyEastNaN3.0East0.0FEETGRADE DOWNHILLMONTROSE RDTILDENWOOD DRNaNROCKVILLENaNDRIVERSAME DIR REAR ENDRAININGWETDAYLIGHTTRAFFIC SIGNALNONE DETECTEDNaNOTHER VEHICLENaNINTERSECTIONFOUR-WAY INTERSECTIONSTRAIGHTNO DEFECTSTWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER39.053807-77.137842(39.05380667, -77.13784167)
79004MCP30730049190050370Montgomery County PoliceInjury Crash10/20/2019 05:30:00 PMYesMaryland (State)NorthNaN3.0North0.0FEETLEVELCONNECTICUT AVEINDEPENDENCE STNaNNaNPEDESTRIANDRIVEROTHERNaNWETDAYLIGHTTRAFFIC SIGNALNONE DETECTEDNONE DETECTEDPEDESTRIANNaNINTERSECTIONFOUR-WAY INTERSECTIONSTRAIGHTNaNTWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER39.076452-77.080452(39.07645167, -77.08045167)
79005MCP3164001K190019195Montgomery County PoliceInjury Crash04/24/2019 01:07:00 PMNoMaryland (State)SouthNaN3.0North0.0FEETLEVELCONNECTICUT AVERANDOLPH RDNaNNaNNaNDRIVERHEAD ON LEFT TURNCLEARDRYDAYLIGHTTRAFFIC SIGNALNaNNaNOTHER VEHICLENaNINTERSECTIONFOUR-WAY INTERSECTIONSTRAIGHTNO DEFECTSTWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER39.057012-77.073737(39.05701167, -77.07373667)
79006MCP2016003C180047875Montgomery County PoliceProperty Damage Crash09/25/2018 12:18:00 PMNoCoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN